Visualizing Graphs and Clusters as Maps

Visualizing Graphs and Clusters as Maps
Emden R. Gansner∗
Yifan Hu†
AT&T Labs - Research, 180 Park Ave, Florham Park, NJ 07932
AT&T Labs - Research, 180 Park Ave, Florham Park, NJ 07932
Stephen G. Kobourov‡
University of Arizona, 1040 E 4th Street, Tucson, AZ 85721
A BSTRACT
Information visualization is essential in making sense out of large
data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not
capture well the underlying structural information, clustering, and
neighborhoods. In this paper, we describe GMap, a practical algorithmic framework for visualizing relational data with geographiclike maps. We illustrate the effectiveness of this approach with examples from several domains.
to understand the visualization of a biological experiment that was
created by a statistician.
When large data sets are drawn as point clouds or node-link
graphs, it is sometimes possible to observe a visual similarity to geographic maps. For example, several small connected components
next to a much larger component suggest several small islands next
to a big continent. We took the next step in visualizing data directly
as a geographic map. A map representation is familiar and intuitive;
most people are very familiar with maps and nicely drawn maps often provide hours of enjoyable exploration. Many people take advantage of this familiarity with maps and manually create map-like
representations that portray relations among abstract concepts. We
were interested in automating the process which begins with the
data and ends with a drawing of a map. In this process there are
two main competing goals: First, we would like to be faithful to the
data, by maintaining the inherent structure and relationships, without adding or implying non-existent structure. Second, we would
like the final result to look like a map, using standard cartographic
conventions. Our attempt to address these two goals led to a framework for a map-based data representation that we call GMap.
Keywords: Information Visualization; Clustering; Graph Drawing; Graph Coloring; Maps; Set Visualization.
I NTRODUCTION
With the growth of the Internet and scientific and technological advances, the world has seen an explosion in the generation and collection of data. This data is often relational, high-dimensional, or
both. With such a wealth of data comes the need to understand it,
analyze it, and use it. Visualization can play a key role in all of
these steps, but especially so in the exploratory phase.
Many useful techniques for visualizing abstract data sets have
been considered in the past. Graphs made of nodes and links (or
edges) are often used to capture the relationships between objects,
and graph drawing allows us to visualize such relationships. Typically nodes are represented by points in two or three dimensional
space, and edges are represented by lines between the corresponding vertices. For an example of a relational data set, consider the
Amazon.com graph, where books are nodes and there is an edge between two books if people who have bought one of the books also
buy the other.
High-dimensional data sets, on the other hand, are often visualized as point clouds in two or three dimensional space. An example
is the listening pattern of users of the last.fm website. Each user
can be represented by a long vector. The dimension of the vector is
the same as the number of musicians available at the website, and
the value of each element is proportional to the number of times a
user listens to the corresponding musician. In a point cloud visualization, each point is a user and two points are close to each other
if the two users have similar music taste.
Unfortunately, these standard approaches of node-link diagrams
and point cloud representations often require considerable effort to
comprehend. If we desire to make the data accessible to people
outside the areas of computer science and statistics, providing more
compelling drawings is an important task. This is certainly true for
the ordinary user who might be curious about the recommendations
made by Amazon.com, but is also true for the biologist who wishes
GM AP
The GMap framework allows us to generate map-like representations from an abstract data set. Specifically, given a highdimensional data set or a graph with edge weights (e.g., the similarity between books as determined by purchase behavior at Amazon.com), it produces a drawing with a map-like look, with countries that enclose similar objects, outer boundaries that follow the
outline of the vertex set, and inner boundaries that have the twists
and turns found in real maps. A typical example is in Figure 1
and shows just under 1000 books.1 Our maps also can have lakes,
islands, and peninsulas, similar to those found in real geographic
maps.
GMap is a framework in the true sense of the word, rather than a
specific algorithm. It consists of four main steps, the first two steps
can be achieved by a variety existing algorithms. For the last two
steps we propose new algorithms.
In the first step we take as input a graph or high-dimensional
data set, and embed it into the plane. The statistics and scientific modeling communities have extensively explored this problem and provide many ways of doing this. Possible embedding
algorithms include principal component analysis, multidimensional
scaling (MDS), force-directed algorithms, or non-linear dimensionality reductions such as Locally Linear Embedding and Isomap.
The second step takes this collection of points in the plane and
aggregates them into clusters. Here, it is important to match the
clustering algorithm to the embedding algorithm. For example, a
geometric clustering algorithm such as k-means may be suitable for
an embedding derived from MDS, as the latter tends to place similar
points in the same geometric region with good separation between
clusters. On the other hand, with an embedding derived from a
∗ e-mail:
[email protected]
[email protected]
‡ e-mail: [email protected]. Work done while this author was at
AT&T Labs.
† e-mail:
1 Most of the images in this article are available in an interactive form at http://www.research.att.com/˜yifanhu/MAPS/
imap.html.
1
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While Europe Slept: How Radical Islam
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Wesley the Owl: The Remarkable Love Story
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Collected Stories
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The Self-esteem Companion: Simple Exercises to Help You Challenge
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The Man From
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Alex & Me: How a Scientist and a Parrot Uncovered a Hidden
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The English
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Night Over
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Water for Elephants:
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The Unbearable Lightness of
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Dante's Divine Comedy: Hell,
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The Consolation of Philosophy:
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Aeschylus I: Oresteia: Agamemnon, The Libation
Bearers, The Eumenides A Heartbreaking Work of
Staggering Genius
Howards End
KFC
Eye of
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Merle's Door: Lessons from
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Dewey: The Small-Town Library Cat
Who Touched the World
Snow Flower and the
Secret Fan: A Novel
Katherine
The Other Boleyn
Girl Enduring Love:
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Amsterdam: A
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The Memory
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Suite Francaise
The Kite
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The Adolescent
The Fall
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Princess The Six Wives of
Henry VIII
Amazing Gracie: A
Dog's Tale
In the Skin of
a Lion
The Gulag Archipelago, 1918-1956: An Experiment
in Literary Investigation, Parts V-VII
The Gulag Archipelago
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The Unbearable Lightness of
Being The Double and
The Gambler
The Aeneid of
Virgil The Cambridge Companion to
Dante Purgatory Euripides V: Electra, The Phoenician
Women, The Bacchae Between the
Acts A Thousand
Splendid Suns
Dead Souls:
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The Plague
The Years
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Novel Grace: A
Novel
The Gulag Archipelago 1918-1956,
Parts I - II
The Gulag Archipelago Volume 1: An
Experiment in Literary Investigation The Gulag Archipelago: 1918-1956, An Experiment
in Literary Investigation III - IV
One Day in the Life
of Ivan Denisovich A Hero of Our
Time Resistance, Rebellion, and
Death: Essays
The New Bloomsday Book:
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Don Quixote
The Gulag Archipelago 1918-1956 Abridged: An
Experiment in Literary Investigation In the First Circle:
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The First
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To the
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The Hours
Heart of
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The Master
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Oblomov
Anthology Of Classical Myth: Primary Sources in Translation : with Additional Translations by Other
Scholars and an Appendix on Linear B sources by Thomas G. Palaima
The Last Days of
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Works Russiana
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Suttree
Ilf and Petrov's American Road Trip: The
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Execution by Hunger: The
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The Solzhenitsyn Reader: New
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Man Is Wolf to
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Ironies of Faith: The Laughter at
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Origen: An Exortation to Martyrdom,
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Leviathan The Republic Of Plato:
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Ever Wonder Why? And
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Pandering Politicians and Misguided Policies that Hurt the Poor
Power/Knowledge: Selected Interviews and
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Rabbit Hole
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New Play
Figure 1: A map of books related to “1984” from Amazon.com
Making the map
force-directed layout, a modularity based clustering [9] could be a
better fit. The two algorithms are strongly related, and therefore we
can expect vertices that are in the same cluster to also be physically
close to each other in the embedding.
Given the placement of the points from the first step, and their clustering from the second, we want to create a map, with inner boundaries separating points not in the same cluster and outer boundaries preferably following the general outline of the point set. A
naive approach for creating the map is to form the Voronoi diagram
of the vertices based on the embedding information, together with
four points on the corners of the bounding box. This is illustrated
in Figure 2(a). Such maps often have sharp corners, and angular
outer boundaries. We can generate more natural outer boundaries
by adding random points to the current embedding. A random point
is only accepted if its distance from any of the real points is more
than some preset threshold. Note that this step can be implemented
efficiently using a suitable space decomposing data structure, such
as a quadtree. This leads to boundaries that follow the shape of
the point set. In addition, the randomness of the points on the outskirts gives rise to some randomness of the outer boundaries, thus
making them more map-like, as seen in Figure 2(b). Furthermore,
depending on the value of the threshold, this step can also result
in the creation of lakes and fjords in areas where vertices are far
apart from each other. Nevertheless, some inner boundaries remain
artificially straight.
At this point, we still note the undesirable feature that the “countries” all have roughly the same area (Figure 2(b)), whereas we
might prefer some areas to be larger than others (e.g., due to the
importance of the entities they represent). As an illustration, in Figure 2, we assume that “node 1” is more important than the other
In the third step, we use the two-dimensional embedding together with the clustering to create the actual map by delineating
country boundaries, carving continental outlines, and separating islands from continents. This can be accomplished with the help of
plane partitioning techniques such as Voronoi diagrams, but with
new algorithmic techniques to ensure realistic looking outer and inner boundaries.
In the fourth and final step, we add additional graphical attributes
to the drawing in order to enhance its clarity, to serve as keys to the
abstract data, or to simply make it more aesthetically appealing.
This would involve assigning an appropriate set of colors to the
various regions. We propose a spectral algorithm that maximize
color difference between neighboring countries. In addition, we
could add mountains to the map or overlay it with a heat map to
indicate scalar values associated with geographic positions.
While the first two steps have been the subject of hundreds of
papers, the last two steps in this process are new. With this in mind,
in the next sections we describe how we put together the embedding
and clustering information so that we can create the map representation and make it aesthetically appealing. Our presentation is narrative and informal. We refer the interested reader to the article [5]
for technical details and more references.
2
(a)
(b)
(c)
(d)
at the Symposium on Graph Drawing. The upper drawing exhibits
the connected components and closely related nodes are indicated
by proximity, but cluster structure is only hinted at. In the GMap
version, the cluster structure is obvious. Coloring the nodes in the
node-link drawing would still only imply the clusters. The GMap
figure makes the clusters explicit as well as indicating strong cluster
relations where two clusters share a border.
When projecting high dimensional data into low dimensional
space, distance distortion is inevitable, and the resulting figure will
often have some anomalies and distortions. Thus, some strongly related objects may be separated by seemingly unrelated objects. For
example, in Figure 3, we see a cluster of North American authors
split into three components in the southwest part of the map (the
three beige components). The authors in the central component
have had much more collaboration with European authors; those
in the bottom-left component much less so. There is a singleton
component sitting in the middle, reflecting the fact that this author,
“North”, collaborated with both the European authors, and with the
group of authors that form the bottom-left component. Such fragmentations are inherent in the embedding and clustering algorithms
used in the first two steps. However we have proposed ways [5] to
use the clustering information to adjust the layout, so that the regions of countries are more contiguous, at the expense of some loss
of relational information captured in the original embedding.
We believe that the map-like visualization better captures some
of the cluster structure while at the same time providing an illustration that is more attractive to the typical user than the traditional
scatterplots and node-link diagrams. Although we have not yet run
a formal user study, anecdotal evidence from our customers and
colleagues corroborates this claim.
Figure 2: (a) Voronoi diagram of vertices and corners of bounding
box; (b) better construction of outer boundaries through placement
of random points; (c) Voronoi diagram of vertices and points inserted
around the bounding boxes of the labels; (d) the final map.
two nodes, and use a larger label for that area. 2 To make areas follow the shape of the labels, we first generate artificial points along
the bounding boxes of the labels as shown in Figure 2(c). To make
the inner boundaries less uniform and more map-like, we perturb
these points randomly instead of running strictly along the boxes.
Here Voronoi cells that belong to the same vertex are colored in the
same color, and cells that correspond to the random points on the
outskirt are not shown. Cells of the same color are then merged to
give the final map in Figure 2(d). Note that instead of the bounding
boxes of labels, we could use any 2D shapes, e.g., the outlines of
real countries, in order to obtain a desired look and proportion of
area, as long as these shapes do not overlap.
We note that not all real maps have complicated boundaries. For
example, boundaries of the western states in the United States often
have long straight sections. We believe that irregular boundaries
are more typical of historical and geographic boundaries, and lead
to more map-like results. But this is a matter of personal taste and
our technique can generate maps of both styles.
When mapping vertices that contain cluster information, in addition to merging cells that belong to the same vertex, we also merge
cells that belong to the same cluster, thus forming regions of complicated shapes, with multiple vertices and labels in each region. At
this point we can add more geographic components to strengthen
the map metaphor. For instance, in places where there is significant
space between vertices in neighboring clusters, we can add lakes,
rivers, or mountain ranges to the map to indicate the distance.
With the regions determined, we have a representation of the
data in which closely related objects, as determined by the graph
topology and possibly edge weights, are drawn closely together.
This geometric information is then used to discover clusters among
the objects. To emphasize the clusters, each is represented as a
collection of geometric regions.
Figure 3 shows the difference between a typical node-link graph
layout and the result of applying GMap to the same data. (Indeed,
the graph layout is first step of the GMap layout.) The data represents the graph of author collaborations between 1994 and 2004
The final step, annotating the map with graphical attributes, involves assigning good colors to the countries in our maps. The
Four Color Theorem states that only four colors are needed to color
any map so that no neighboring countries share the same color. It
is implicitly assumed that each country forms a connected region.
This result, however, is of limited use to us because countries in our
maps are not always connected. Therefore, we will have to use one
unique color for each cluster to avoid ambiguity. Estimation of the
number of colors an “average human” can discriminate, when color
pairs are presented side by side, ranges from tens of thousands to a
million. On the other hand, it takes much more effort when comparing colors that are similar. A further limiting factor is that 5%
of males are color blind, which rules out certain coloring schemes.
Finally, the limited palette of map-like coloring schemes reduces
the choice of colors even more.
In GMap, we start with a coloring scheme from ColorBrewer
(www.colorbrewer.org), and generate as many colors as the
number of countries by blending the base colors. As a result, our
color space is linear and discrete. Because of the blending, any
two consecutive colors in the linear array of colors are similar to
each other. When applying these colors to the map, we want to
avoid coloring neighboring countries with such pairs of colors.3
With this in mind, we need to solve a discrete optimization problem
where we find the best color permutation such that the color difference between neighbors is maximized overall. This is achieved by
a combination of two procedures. They both operate on the country
graph. The country graph is created from the original graph after it
has been embedded and clustered. Specifically, the country graph
contains a node for each country, with each edge representing two
2 A weighted Voronoi diagrams can be used to make the area of each
Voronoi cell proportional to its weight. We do not use this approach, however, because we want the Voronoi cell to also contain a specific shape, e.g.,
the bounding box of a label.
3 Although two non-neighboring countries with similar colors can lead
the viewer to believe that they are disjoint regions of the same country, this
problem diminishes when the two countries are sufficiently far apart, as it is
unlikely that distant regions belong to the same cluster.
Coloring the map
3
Figure 3: Node-link drawing compared to the GMap representation
4
Based on this style of use, GMap figures are most effective when
displayed as a large image, often a meter or more in width, or via an
interactive viewer. In the former case, the user can physically move
to change the scale. In the latter case, the viewer provides the scale
change and, at the same time, can provide some version of semantic
zoom, so that more detail is added the more the user zooms in. In
addition, an interactive viewer can provide such additional features
as textual search or links connecting a feature on the map to some
external information. For example, clicking on a book shown in the
BookLand layout might take the user to its entry at Amazon.com.
Given the page size limitations of a traditional journal, the figures included here are scaled to illustrate some features of the
GMap layout (Figures 4 and 8) or to give a high-level view of such
maps. The reader is encouraged to explore some GMap layouts at
the site indicated in Footnote 1.
We now consider how GMap works in practice with various different data sets. In the first, we visualize the “landscape” of music
artists. In the second, we visualize the “landscape” of books as
implied by user purchase behavior at Amazon.com. In the third,
we visualize international trade data. Finally, we look at TV programs based on users’ viewing habits. In our implementation we
use a scalable force-directed layout algorithm for step one and a
modularity-based clustering algorithm for step two.
It is important to note that in all cases the countries and their
geography in the resulting maps are not part of the input data, but
emerge from the graph layout and clustering algorithms. This gives
the user a potential tool to discover structure based solely on local
data.
countries that share a border. The first procedure turns the map coloring problem into that of a continuous optimization problem on
the country graph,
max
∑
{i, j}∈Ec
wi, j (ci − c j )2 , subject to
∑ ck
= 1,
(1)
k∈Vc
where Ec is the set of edges in the country graph, scalar value ci
is the color (index) assigned to country i, and weighting factor wi, j
measures how important it is to promote color difference between
country i and country j. The solution is the largest eigenvector of
the weighted Laplacian of the country graph. We then use the ordering induced by the values of the eigenvector as the color permutation. This is then followed by a greedy color swapping procedure
to see if the color difference can be further improved. This heuristic
algorithm improves the color difference between neighbors significantly compared with a random color assignment.
Figure 4 illustrates the difference between a simple, random coloring and a coloring obtained using our approach. Note, for example, that in the random coloring, the countries in the middle are
assigned colors in a green-gray palette, making it difficult to distinguish them. In the lower, optimized coloring, the separate regions
in the central part are much more evident.
U SING GM AP TO V ISUALIZE S ET R ELATIONS
GMap is designed for visualizing cluster relations as maps, where
each item is assumed to belong to one cluster and hence one country. However, the same algorithm can be easily adapted to visualize multiple relations among a set of objects. Byelas and
Telea [3] proposed visualizing set relations using deformed convex
hulls. Collins et al. [4] proposed “bubblesets,” based on isocontours. GMap provides a different approach.
To apply GMap for visualizing sets, we work on one set at a time.
Items not in the active set are considered obstacles, and treated the
same as random points inserted in the GMap algorithm. Applying
GMap then gives us a map of one country. By repeating this process
for multiple sets, and using transparency to allow overlapping parts
of the regions to be seen, we achieve a visualization of multiple
sets.
To avoid a country being disconnected, we add edges to link
items in the same set. The edge addition process is similar to that
of Collins et al. [4]. Edges are then routed as splines to avoid hitting items not in the active set, when possible. Artificial points are
inserted along the spline edges. When GMap is applied, distant
items are connected by “bridges” along the edges. Figure 5 shows
a group of 55 photos from the Yale Face Database [7], representing portraits using different expressions and lightings. The photos
are embedded in 2D using MDS. The distance between two photos
is calculated using principal component analysis of a matrix of 55
rows, with each row a vector of the pixel values of a photo. Even
with careful pre-processing, the embedding does not always put the
same persons in the same neighborhood. Without set visualization,
it is difficult to identify all photos of the same person. Figure 5
shows three sets of photos, each set belonging to a single person.
As can be seen, each country is connected, and each avoids photos not in the set. This example shows that GMap provides a good
alternative for set visualization.
MusicLand
To create a land of music, we collected data from a web crawl
of the last.fm website. As an Internet radio and music
community website, it has over 30 million users, and recommends music based on user profiles. Over several years, the
recommender system has collected information about how one
band/musician/composer/artist is related to another in terms of how
many listeners of one also enjoy the other. For each composer,
the last.fm website lists the top 250 related composers. For example, Beethoven is considered to have “super similarity” to Mozart,
Bach, and Brahms, “very high similarity” to Mendelssohn, Schumann, and Vivaldi 4 The website also provides the number of listeners of each musician. In April 2009, we crawled the website
by starting with Beethoven, and the top 20 musicians most similar to Beethoven, provided that each has at least 100,000 listeners5 . We then found the top 20 most similar musicians to each of
those with at least 100,000 listeners and proceed recursively. Our
crawl yielded a graph with 2782 musicians, with edge weights corresponding to the similarity between musicians. We further pruned
this graph by only taking edges that have “super similarity”. Finally, ignoring singletons, we end up with 2588 vertices. We then
laid out the graph, clustered the vertices, and generated the MusicLand map as shown in Figure 6. We next examine some of the
countries in MusicLand in more detail.
The Mainland: The vast majority of musicians/bands is located
in a single continent. While it is not as easy to spot major trends
along the main axes, many of the clusters are well-defined and
neighboring clusters make sense from a musical point of view. A
cluster of classic rock on the east shore begins with Eric Clapton
and Janis Joplin in the south, goes through the Who in the middle
GM AP C ASE S TUDIES
The GMap layout is meant to accentuate clusters in fairly large
graphs. Gleaning information from the maps typically involves an
interactive, multi-scale process, similar to that used for exploring
geographic maps. One views the map at small scale to sense the
overall layout, the major regions, and how they relate to each other.
One then zooms in to see local detail, and to traverse the map along
small features. At some point, one may zoom out again to put the
local details into a global context.
4 Note
that we do not know last.fm’s formal definition of “super similarity”, “very high similarity”, or “high similarity”.
5 The reason we added a cut off of 100,000 listeners is that the number
of listeners of musicians seem to follow a power law distribution. Without
the cut off, our crawl did not finish after over a week of crawling, during
which time we observed well over 1/2 million musicians, many with only a
few hundred listeners.
5
Figure 4: A random color assignment (top) vs. an optimized color assignment (bottom) that maximizes color differences between neighbors
6
Figure 5: Using GMap to visualize multiple sets. Photos in the same set belongs to the same person.
before reaching the heavy rock cluster of AC/DC and Iron Maiden
in the north. Farther to the north is the grunge cluster anchored by
Nirvana and Alice in Chains.
On the west coast of the main continent, we find a cluster of electronic music, featuring Fatboy Slim, Daft Punk, and DJ Shadow. To
the north are avant-garde electronic bands like Aphex Twin, while
in the extreme west are electronic classics such as Vangelis. To the
south is a compact and well-defined cluster of dance music represented by Paul Oakenfold and ATB.
In the center of the map, there is a well-defined concentration of
female singer-songwriters such as Alanis Morissette, Norah Jones,
and Amy Winehouse. Pop music is to the southeast of here with
ABBA and Eurythmics, while hip-hop and rap music is southwest
with Beyonce and Alicia Keys.
The Islands: There are two notable island regions in MusicLand: in the northeast is Reggae island, while the chain of islands
off Rocky Coast in the southeast make up the Classical Archipelago.
By zooming in (Figure 7), it is easy to find some general patterns in
the layout of the archipelago along the East-West and North-South
axes. Along the first axis, the west contains modern composers
such as Ravel, Satie, and Pärt, while the east contains 17th century
composers such as Bach, Handel, and Albinoni. Along the second
axis, the north has a high concentration of opera composers such as
Verdi, Rossini, and Puccini, whereas the south has more orchestral
and instrumental composers such as Holst, Elgar, and Stravinsky.
Not surprisingly, Mozart and Beethoven are the most popular composers in the classical music cluster. The islands of Erik Satie and
Arvo Pärt connect the big island in the east with the westernmost
island of contemporary classical music represented by minimalists
Philip Glass and Michael Nyman.
BookLand
Many e-commerce websites provide recommendations to allow for
exploration of related items. Traditionally this is done in the form
of a flat list. For example, Amazon typically lists around 5-6 books
under “Customers Who Bought This Item Also Bought”, with a
clickable arrow to allow a customer to see further related items.
Instead of a flat list, which provides a very limited view of the
7
Madness
The Specials
The Skatalites
Bob Marley &
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Desmond Dekker
UB40
Toots and
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Shaggy
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Die Fantastischen
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Jr.
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Télépopmusik
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Klaxons
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Albert
ATB
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New York
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The Velvet
Underground
Jewel
Meredith Brooks
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Billy Idol
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Bob Dylan
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Kid Rock
After Forever
Tristania
Kreator
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The Darkness
Black Label
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Annihilator
Sirenia
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Xandria
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HammerFall
Judas Priest
Blind Guardian
Edguy
Manowar
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Twisted Sister
DragonForce
Dio
Accept
Mötley Crüe
Within Temptation
Kamelot
Apocalyptica
Stratovarius
Gamma Ray
Running Wild
Hanoi Rocks
KISS
Epica
Avantasia
Helloween
Saxon
White Lion
Black Sabbath
Iced Earth
Nightwish
Evergrey
Dream Theater
Nevermore
Rage
Scorpions
Sex Pistols
Bon JoviDeep Purple
The Undertones
The Damned
Symphony X
Pain of
Salvation
Ayreon
Riverside
Liquid Tension
Experiment
Ramones
Buzzcocks
Roxy Music
Velvet Revolver
Rainbow
Van Halen
Stiff Little
Fingers
The Stranglers
Tarja Turunen
Demons &
Wizards
Guns N'
Roses
Whitesnake
Haggard
Angra
Primal Fear
Europe
Alice Cooper
Aerosmith
The Jam
Therion
Bruce Dickinson
AC/DC
David Lee
Roth
Def Leppard
The Clash
Edenbridge
Evanescence
Leaves' Eyes
Ozzy Osbourne
W.A.S.P.
The Cult
The Sisters
of Mercy
Bow Wow
Wow
XTC
The Gathering
Anthrax
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the Banshees
Paradise Lost
Theatre of
Tragedy
Extreme
Ratt
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The Cure
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The Boomtown
Rats
Public Image
Ltd.
My Dying
Bride
Anathema
Slayer
Exodus
Metallica
Venom
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the Ants
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Pantera
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Joe
Warrant
Poison
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The Fall
The 69
Eyes
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Negative
Backyard Babies
Sunrise Avenue
2raumwohnung
Mr. Big
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Bono
Nick Cave
Danzig
HIM
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Motörhead
Wire
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Devo
Poets of
the Fall
The Hellacopters
mia
Wir sind
Helden
Andrew W.K. Iron Maiden
Annett Louisan
Rosenstolz
Paul Weller
Orchestral Manoeuvres in
the Dark
Prince &
The Revolution
Prince
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Mission of
Burma
David Byrne
The Church
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Machine Head
Sepultura
The Rasmus
CKY
Juli
Linkin Park
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Joy Division
New Order
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Blondie
Simple Minds
Patti Smith
Nico
Gang of
Four
INXS
The Psychedelic
Furs
Tom Waits
Sparks
Electronic
Heaven 17
Ani DiFranco
Tracy Chapman
U2
Skunk Anansie
Nick Cave and the
Bad Seeds
The The
The Stooges
Sheryl Crow
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Fort Minor
Talking Heads
Television
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John Mayer
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Robyn
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The Beautiful
South
The Be
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Zandt
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Something
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Hole
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The Cardigans
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DJ Danger
Mouse
Lasgo
DJ Sammy
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Dahl
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Love Affair
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Buuren
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Live
The Last
Goodnight
Vertical Horizon
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Auf der
Maur
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Train
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UNKLE
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Say Anything
Circa Survive
Thursday
Brand NewAutopilot Off
Black Rebel
Motorcycle Club
Sonic Youth
The Bravery
Little Man
Tate
AFI
Spitalfield
The Early
November
Sherwood
Ben Folds
Five
Rocky Votolato
Sondre Lerche
Queens of the
Stone Age
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Blood Red
Shoes
Good Shoes
Bishop Allen
Death From
Above 1979
Sleater-Kinney
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Pornographers
Lady Sovereign
Diplo
Ulrich Schnauss
Telefon Tel
Aviv
DJ Shadow
Orbital
Llorca
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Talvin Singh
Eluvium
Caribou
Tiefschwarz
Aim
Jon Kennedy
Red Snapper
Parov Stelar
Hird
Beanfield
Nicola Conte
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Wax Tailor
DJ Cam
Nightmares on
Wax
William Orbit
Mo' Horizons
The Funky
Lowlives
Nitin Sawhney
Blonde Redhead
The National
The Honorary
Title
Idlewild
The Von
Bondies
The Kooks
Voxtrot
The Brian
Jonestown Massacre
Glassjaw
Senses Fail
Franz Ferdinand IMA Robot Beck
The Cinematics
Rogue Wave
Straylight Run
Further Seems
Forever
The Perishers
Death Cab
for Cutie
Cold War
Kids
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Knives
The Spinto
Band
Arcade Fire
Louis XIV
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The Rascals
Yeah Yeah
Yeahs
Midlake
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Club
Animal Collective
Rodrigo y
Gabriela
RJD2
Micatone
LTJ Bukem
Delerium
Fink
Quantic
The Orb
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of London
Koop
Kyoto Jazz
Massive
Funki Porcini
The Cinematic
Kid Koala
Orchestra
The KLF
Banco de
Gaia
Jaga Jazzist
Bugge Wesseltoft
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DJ Krush
4hero
Wolf Parade
Oh No!
Oh My!
Islands
Frightened Rabbit
Manitoba
808 State
Skalpel
The Ordinary
Boys
Dr. Dog
A.C. Newman
Beulah
The Magnetic
Fields
múm
David Holmes
The Clientele
Spoon
The Postal
Service
The Walkmen
Håkan Hellström
The Most
Serene Republic
Dntel
The Microphones
Dead Poetic
Thrice
Rise Against
Bayside
The Get
Up Kids
Daphne Loves
Derby
Shiny Toy
Guns
The Last
Shadow Puppets
The Beta
Band
Babyshambles
Saves the
Day
The Juliana
Theory
Monster Magnet
The Notwist
Sebadoh
The Hives
The Subways
The Dead
60s
Modest Mouse
The Gossip
Four Tet
Apparat
The Herbaliser
Blockhead
The Quantic
Soul Orchestra
The Album
Leaf
Amiina
Prefuse 73
Arctic Monkeys
Mando Diao
Lambchop
Tocotronic
Kyuss
Ben Folds
Slut
The Polyphonic
Spree
Grandaddy
Ambulance LTD
Tapes 'n
Tapes
TV on
the Radio
Deerhunter
Seabear
Le Tigre
Ellen Allien
& Apparat
Booka Shade
The Fall
of Troy
No Use for
a Name
Fu Manchu
The Cooper
Temple Clause
Nine Black
Alps
Mercury Rev
Bright Eyes
The Bled
Strike Anywhere
Bad Religion
Pennywise
The Sounds
The Appleseed
Cast
The Kills
Stephen Malkmus
Interpol
Johnossi
Glasvegas
Moneybrother
Nathan Fake
DJ Food
Tiger Lou
Be Your
Own Pet
Ugly Casanova
Sunset Rubdown
Stars of
the Lid
Spank Rock
Gui Boratto
DJ Vadim
Coldcut
Alice Russell
Neutral Milk
Hotel
MUM
LFO
Daedelus
Her Space
Holiday
Apostle of
Hustle
Efterklang
Autechre
Arovane
Luke Vibert
Roots Manuva
Up, Bustle
and Out
Menomena
Danny Elfman
AFX
Plaid
Ricardo Villalobos
Wagon Christ
The Hold
Steady
Test Icicles
James Newton
Howard
Comeback Kid
Fear Before the March
of Flames
Norma Jean
Anti-Flag
Strung Out
Elefant
Sugarplum Fairy
µ-Ziq
Mouse on
Mars
Propagandhi
Good Riddance
Millencolin
Radiohead
Red House
Painters
Sparklehorse
The American
Analog Set
The Wrens
Sahara Hotnights
Pavement
British Sea
Power
The Stills
Enon
John Vanderslice
Venetian Snares
Built to
Spill
Pinback
Every Time
Blindside I Die
Descendents
Lagwagon
Clutch
Lali Puna
The Faint
Silver Jews
She Wants
Revenge
Clinic
The Black
Heart Procession
Caesars
The Dears
Deerhoof
Ted Leo and
The Pharmacists
The Black
Keys
Kent
James Horner
John Williams
Harry Gregson-Williams
Thomas Newman
Aphex Twin
Ben Gibbard
Youth Group
Mogwai
The Bouncing
Souls
Me First and the
Gimme Gimmes
Mastodon
Guided by
Voices
Calexico
The Bird and
the Bee
The Dillinger
Escape Plan
Jonny Greenwood
Biffy Clyro
Smog
The Thermals
The Blood
Brothers
NOFX
Between the Buried
and Me
Tunng
Cursive
Tortoise
Les Savy
Fav
Destroyer
Rancid
Goldfinger
Neurosis
Psapp
The Horrors
Sparta
Stereolab
This Will
Destroy You
Explosions in
the Sky
Xiu Xiu
Klaus Badelt
Jerry Goldsmith
Isis
Silversun Pickups
dredg
The Sea
and Cake
Godspeed You!
Black Emperor
You Say Party! We
Say Die!
Casiotone for the
Painfully Alone
Meshuggah
At the
Drive-In ...And You Will Know UsThom Yorke
The Donnas
by the Trail of...
Oceansize
Bernard Herrmann
mewithoutYou
The Vandals
The Suicide
Machines
Less Than
Jake
The Good
Life
Q and
Not U
A Silver
Mt. Zion
Broadcast
John Barry
Ennio Morricone
Operation Ivy
Catch 22
God Is
Pelican
an Astronaut
Steel Pulse
Augustus Pablo
"Weird Al"
Yankovic
Converge
Mad Caddies
Reel Big
Fish
Slint
Hot Water
Music
South Park
The Upsetters
Culture
Alpha Blondy
King Tubby
Monty Python
Transplants
The Mighty
Mighty Bosstones
Dennis Brown
Horace Andy
Adam Sandler
Israel Vibration
The Aquabats
Richard Cheese
Gregory Isaacs
Black Uhuru
Sizzla
Dane Cook
The Exploited
Dead Kennedys
Dido
Tears for
Sting
Bryan Adams Black Flag
The Cranberries
Fears Elvis Costello
Eurythmics
Britney Spears
Gwen Stefani
The Who Slade
a-ha Bruce Springsteen
Deltron 3030
Madonna
Cyndi Lauper
Bebel Gilberto
Beastie Boys
Level 42
Golden Earring
Mr. Mister Jim Croce
Billy Ocean
Fergie Beyoncé
Amy Winehouse Chic
A Tribe
Djavan Aesop Rock
Billy Joel
The Kinks
Jimi Hendrix
Called Quest
Tom Petty
Antônio Carlos
Air Supply
Black Eyed Kelis
Alicia Keys Norah Jones
ABBA
Cream
Jobim
Genesis
The Doors
Peas
Jamiroquai
Dire Straits
James Brown
Aaliyah
Busta Rhymes
Lauryn Hill
Akon Ne-Yo
Celia Cruz Diana Krall
Janis Joplin
Aretha Franklin
Jethro Tull
Stevie Wonder
Roy Orbison Eric Clapton
ShakiraBabyface
T.I.
Freddie Hubbard
2Pac
Rank 1
Darude
Novaspace
Andain
Motorcycle
La Bouche
Groove Coverage
Safri Duo
Marco V
Pulsedriver
iiO
4 Strings
Special D.
Scooter
Gal Costa
A*Teens
Blue
Cypress Hill
DANGERDOOM
Savage Garden
Darren Hayes
Westlife
The Brand
New Heavies
Nas
Public Enemy
Us3
Eve
LL Cool
J
Lil' Kim
Missy Elliott
Keyshia Cole
Tweet
Salt-N-Pepa
Jadakiss
Art Blakey & The
Jazz Messengers
TLC
Chris Brown
John Legend
Nelly
D'Angelo
Lloyd Banks
Chingy
G-Unit
K-Ci &
JoJo
Craig David
Miles Davis
Obie Trice
Bubba Sparxxx
Keith Jarrett
Bill Evans
112
Cam'ron
D12
Herbie Hancock
Trick Daddy
Juelz Santana
Kenny Chesney
Toby Keith
Amaral
Aterciopelados
Blackstreet
Harry Connick,
Jr.
Nancy Wilson
Sarah Vaughan
Dinah Washington
Ella Fitzgerald
Lou Rawls
Carmen McRae
Jarabe de
Palo
Anita O'Day
Otis Redding
Nat King
Cole
John Lennon
Elvis Presley
Solomon Burke
Yes
Carl Perkins
Enanitos Verdes
Rosemary Clooney
Jimmy Buffett
Sammy Davis,
Jr.
Bing Crosby
Bill Haley and
the Comets
Little Richard
Duke Ellington
King
Soda Stereo
Wes Montgomery
Daddy Yankee
Chambao
Amparanoia
Don Omar
Django Reinhardt
Count Basie Benny Goodman
Ojos de
Brujo
Cab Calloway
Josh Groban
Bobby Darin
Vince Guaraldi
Trio
Judy Garland
The Alan
Parsons Project
Emerson, Lake
& Palmer
Paul McCartney
& Wings
Johann Strauss
II
Richard Wagner
Richard Strauss
Wings
Eddie Cochran
The Blues
Brothers
Percy Sledge
Frédéric Chopin
Pyotr Ilyich
Tchaikovsky
Franz Liszt
Michael Nyman
The Drifters
Hector Berlioz
Claude Debussy
Robert Schumann
Gene Vincent
Charlie Feathers
Chubby Checker
Ella Fitzgerald &
Louis Armstrong
Joaquín Sabina
The Platters
Dean Martin
Three 6
Mafia
Mahavishnu Orchestra
Peter Gabriel
Del Shannon
Booker T. &
The MG's
Gil Scott-Heron
Julie London
Melendi
Louis Prima
Jerry Lee
Lewis
Ricky Nelson
Perry Como
Lena Horne
Bebe
Los Rodríguez
Gioacchino Rossini
The Beatles
Albert King
Fats Domino
The Everly
Brothers
Blossom Dearie
Mel Tormé
Tony Bennett
George Benson
Andrés Calamaro
Giacomo Puccini
Georges Bizet
Canned Heat
B.B. King &
Eric Clapton
Louis Armstrong
Frank Sinatra
Los Fabulosos
Estopa
Doris Day
Cadillacs
Ben E.
Pitbull
Lil Jon
Lil Jon & The
East Side Boyz
Giuseppe Verdi
John Mayall &
The Bluesbreakers
Phish
Ringo Starr
Sam &
Dave
The Lemonheads
Grateful Dead
Rush
The Monkees
Procol Harum
George Harrison
Buddy Holly
Chuck Berry
Jackie Wilson
Iron Butterfly
Stevie Ray Vaughan and
Double Trouble
Johnny Winter
Stevie Ray
Vaughan
Bo Diddley
The Shirelles
The Righteous
Brothers
El Canto
del Loco
Café Tacuba
Ying Yang
Twins
Chick Corea
Al Di
Meola
Maná
Rory Gallagher
The Posies
Big Brother & The
Holding Company
John Mayall
Focus
Etta James
Brian Wilson
Teenage Fanclub
Steely Dan
The Mamas &
The Papas
Herman's Hermits
J.J. Cale
Buddy Guy
Ray Charles
Paul McCartney
Peggy Lee
Robert Plant
Pink Floyd
Syd Barrett
Derek and
The Dominos
Supertramp
Manfred Mann
Van der
Graaf Generator
Camel
Nina Simone
Alejandro Sanz
The Beach
Boys
Jefferson Airplane
Fountains of
Wayne
Ten Years
After
Gentle Giant
Dr. John
Howlin' Wolf
Andy Williams
Petey Pablo
Bone Thugs-N-Harmony
Chet Baker
Weather ReportEminem
Jaco Pastorius
La Oreja de
Van Gogh
Belanova
Eartha Kitt
Blind Faith
Mark Knopfler
The Moody
Blues
The Ronettes
The Meters
Edwin Starr
Sam Cooke
Wilson Pickett
Shirley Horn
Michael Bublé
Al Jarreau
Sin Bandera
Boyz II
Men
Jagged Edge
Young Buck
Thelonious Monk
Smokey Robinson and
the Miracles
The Marvelettes
Isaac Hayes
Lonestar
Keith Urban
Julieta Venegas
Vanilla Ice
Soulja Boy
Warren G
The Lovin'
Spoonful
The Hollies
King Crimson
Alan Jackson
Ricardo Arjona
Lloyd
Snoop Dogg
Jim Jones
The Four
Tops
Al Green
Bobby Womack
Roberta Flack
The Black
Crowes
The Jimi
Hendrix Experience
David Gilmour
Bill Withers
Curtis Mayfield
Cassandra Wilson
Joe Cocker
The Turtles
John Lee
Hooker
The Temptations
The Spinners
Donny Hathaway
Roy Ayers
Natalie Cole
Peter Cincotti
Page &
Plant
Roger Waters
13th Floor
Elevators
The Animals
B.B. King
Robert Johnson
The O'Jays
Minnie Riperton
Billie Holiday
Jane Monheit
Garth Brooks
Brad Paisley
Led Zeppelin
Big Star
The Allman
Brothers Band
Chris Rea
Muddy Waters
War
The Supremes
The Troggs
Traffic
Sly & The
Family Stone
Gladys Knight &
The Pips
The Isley
Brothers
Shirley Bassey
Jamie Cullum
Rascal Flatts
Reik
Juanes
Joe
Bad Company
Creedence Clearwater
Revival
Love
Kansas
The Zombies
Leadbelly
Tim McGraw
Lizz Wright
Charles Mingus
Ike &
Tina Turner
Tom Petty and
the Heartbreakers
Funkadelic
Luis Miguel
Chayanne
Brian McKnight
Marques Houston
Ghostface
Donald Byrd
Commodores
Stacey Kent
Luther Vandross
America
The Rolling
Stones
Electric Light
Orchestra
The Spencer
Davis Group
Toto
Elton John
Marc Anthony
Ricky Martin
Ginuwine
Mario
R. Kelly
Ray J
Chamillionaire
Dizzy Gillespie
Taj Mahal
Phil Collins
Martha Reeves &
The Vandellas
Marvin Gaye
Paulina Rubio
Fugees
Bobby Valentino
Omarion
Mims
Rick Ross
Diana Ross and
The Supremes
Free
The Guess
Who
The Yardbirds
The Byrds
Fleetwood Mac
Kenny Rogers
Parliament
Nancy Sinatra
Chaka Khan
Madeleine Peyroux
Randy Crawford
Anthony Hamilton
Baby Bash
Bow Wow
Twista
Coolio
Young Jeezy
Xzibit
Anita Baker
Angie Stone
En Vogue
Musiq
Flo Rida
Earth, Wind
& Fire
Barry White
Faith Hill
Petula Clark
ZZ Top
Peter Frampton
The Doobie
Brothers
Eagles
Chicago
Ry Cooder
Michael Bolton
Cameo
Dusty Springfield
Buffalo Springfield
Three Dog
Night
Lynyrd Skynyrd
Gordon Lightfoot
Ohio Players
Dionne Warwick
Macy Gray
Erykah Badu
Maxwell
Wyclef Jean
Jamie Foxx
Usher
MC Hammer
Ja Rule
50 Cent
McCoy Tyner
Robin Thicke
Will Smith
T-Pain
Lil' Wayne
The Game
DMX
Charlie Parker
Crosby, Stills
& Nash
Bob Seger
Rod Stewart
Asia
Richard Marx
Barbra Streisand
Barry Manilow
India.Arie
Mary J.
Blige
Faith Evans
Jill Scott
Sean Kingston
Sir Mix-A-Lot
Fat Joe
Eazy-E
John Coltrane
Wayne Shorter
Ol' Dirty
Bastard
Bee Gees
Rick James
Kool &
The Gang
Lionel Richie
Sade
Gloria Estefan
Tamia
Don Henley
Status Quo
Gary Moore
Steppenwolf
Warren Zevon
Survivor
Hall &
Oates
Mike &
The Mechanics
Cliff Richard
Shania Twain
Toni Braxton
P. Diddy
Puff Daddy
Ice Cube
Method Man
& Redman
Fabolous
Method Man
RZA
Hot Chocolate
Wild Cherry
Thalía
Madcon
Dr. Dre
Raekwon
Monica
Timbaland
M.O.P.
Sonny Rollins
Mya
The J.
Geils Band
John Mellencamp
Eddie Money
Steve Winwood
Neil Diamond
Kenny Loggins
Chris de
Burgh
Diana Ross
Des'ree
Grand Funk
Railroad
Steve Miller
Band
Neil Young
Journey
REO Speedwagon
Jackson Browne
Stevie Nicks
Woody Guthrie
John Denver
Smokie
Art of
Noise
Carpenters
Lisa Stansfield
Joss Stone
LeAnn Rimes
Martina McBride
Enrique Iglesias
Naughty by
Nature
Lee Morgan
Horace Silver
Janet Jackson
Brandy
Don McLean
John Hiatt
Cutting Crew
Starship
Modern Talking
Gloria Gaynor
Keri Hilson
will.i.am
Ludacris
Wu-Tang Clan
Estelle
Bachman-Turner Overdrive
Camouflage
Paul Young
Bonnie Tyler
Wet Wet
Wet
Olivia Newton-John
Boney M.
KC and the
Sunshine Band
Tina Turner
Bad Brains
Nazareth
UFO
Thin Lizzy
Neil Young &
Crazy Horse
Heart
Huey Lewis &
The News
Pat Benatar
Kim Carnes
Carly Simon
Irene Cara
Sister Sledge
Village People
George Michael
Céline Dion
Amerie
Run-D.M.C.
N.W.A
Yello
Laura Branigan
Sandra
Wham!
Paula Abdul
Sarah Connor
Dixie Chicks
Mobb Deep
Cannonball Adderley
Guru
Christina Milian
Donovan
Crowded House
Lipps, Inc.
Eva Cassidy
Corinne Bailey
Rae
Mariah Carey
Roxette
Pharrell
Notorious B.I.G.
Big L
Rick Astley
The Pointer
Sisters
Belinda Carlisle
Simply Red
Gabrielle
Carrie Underwood
Lisa Ekdahl
Justin Timberlake
Dexter Gordon
Redman
The Bangles
Seal
Alison Krauss
Taylor Swift
Katie Melua
Anastacia
JoJo
Eric B.
& Rakim
Dave Brubeck
GZA/Genius
Vanessa Hudgens
Katharine McPhee
Destiny's Child
Ashanti
Foghat
Joe Walsh
Crosby, Stills, Nash
& Young
Bonnie Raitt
Robert Palmer
Cher
Pharoahe Monch
The Dave
Brubeck Quartet
Ashley Tisdale
Jordin Sparks
Christina Aguilera
Jennifer Lopez
Cherish
Kelly Rowland
Whitney Houston
KRS-One
Vanessa da
Mata
Van Morrison
Donna Summer
Nelly Furtado
Cassie
Jennifer Hudson
Sweet
Boston
Humble Pie
Cat Stevens
James Taylor
Culture Club
Uriah Heep
Cheap Trick
Styx
Foreigner
The Knack
Simon &
Garfunkel
Gary Numan
Bananarama
Minor Threat
Ted Nugent
Jon Bon
Jovi
The Small
Faces
The Band
Meat Loaf
Midnight Oil
Howard Jones
Nik Kershaw
The Sonics
Alison Moyet
Bette Midler
Leona Lewis
Jamelia
Kat DeLuna
Michelle Williams
*NSYNC
Kanye West
Ciara
Incognito
Astrud Gilberto
Jessica Simpson
The Pussycat
Dolls
Rihanna
Danity Kane
Lupe Fiasco
OutKast
Bob Dylan and
The Band
The Cars
The Police
Men at
Work
Talk Talk
Alphaville
Chris Isaak
Carole King
Blue Öyster
Cult
Bryan Ferry
Joe Jackson
Elvis Costello &
The Attractions
The Pretenders
Spandau Ballet
The Go-Go's
Fine Young
Cannibals
Marianne Faithfull
Grace Jones
Miley Cyrus
Robbie Williams
Jay-Z
Q-Tip
Berlin
A Flock
of Seagulls
Japan
Paul Simon
Frankie Goes
to Hollywood
Dead or
Alive
Erasure
Pet Shop
Boys
Duffy
Aly &
AJ
Mandy Moore
Shayne Ward
Ronan Keating
Nicole Scherzinger
Solange
Common
The Roots
Mos Def
Clipse
Masta Ace
Paris Hilton
Men Without
Hats
ABC
Yazoo
Soft Cell
Annie Lennox
Jedi Mind
Tricks
Terence Trent
D'Arby
The Pharcyde
Murs
Gang Starr
Los Hermanos
Natasha Bedingfield
Stacie Orrico
Thompson Twins
The Buggles
Visage
dead prez
k.d. lang
Kate Bush
Lighthouse Family
Backstreet Boys
Talib Kweli
De La
Soul
Arrested Development
Dilated Peoples
Stan Getz
Sinéad O'Connor
Lindsay Lohan
No Doubt
Gnarls Barkley
Black Star
Little Brother
Atmosphere
CunninLynguists
Sage Francis
The Wreckers
Daniel Bedingfield
N*E*R*D
Jurassic 5
Chico Buarque
El-P
Hilary Duff
Jesse McCartney
P!nk
Mutya Buena
Boyzone
House of
Pain
Ace of
Base
Ultravox
The Human
League
Kim Wilde
Immortal Technique
Indigo Girls
Melissa Etheridge
Dolly Parton
Pink
Adele
Melanie C
Blackalicious
Hieroglyphics
Del tha
Funkee Homosapien
Elis Regina
Marisa Monte
Ashlee Simpson
Delta Goodrem
Emma Bunton
Spice Girls
Joni Mitchell
Flobots
Emmylou Harris
Texas
Kelly Clarkson
Kelly Osbourne
S Club
7
Dr. Octagon
Caetano Veloso
Gilberto Gil
Madlib
Jorge Ben
Sarah McLachlan
Colbie Caillat
Will Young
Lucie Silvas
Geri Halliwell
Atomic Kitten
September
Infernal
Jonas Brothers
Madvillain
Quasimoto
O-Zone
Cibelle
João Gilberto
Lou Reed
Joan Osborne
Take That
All Saints
The Corrs
Paris Combo
Handsome Boy
Modeling School
MF DOOM
Gigi D'Agostino
K's Choice
Natalie Imbruglia
Gabriella Cilmi
Rachel Stevens
Dannii Minogue
Girl Talk
Alcazar
Alice Deejay
Brooklyn Bounce
Eiffel 65
Kylie Minogue
Pink Martini
Cascada
Dr. Alban
Everlast
Sugababes
Kate Ryan
Basshunter
DJ Shog
Technotronic
Camille
Vanessa Paradis
Nouvelle Vague
Culture Beat
2 Unlimited
Haddaway
Snap!
Mauro Picotto
Bomfunk MC's
Zbigniew Preisner
Arvo Pärt
Kronos Quartet
Erik Satie
Franz Schubert
Edvard Grieg
Wolfgang Amadeus
Mozart
Gustav Mahler
Jean Sibelius
Felix Mendelssohn
Maurice Ravel
Philip Glass
Brenda Lee
Ludwig van
Beethoven
Johannes Brahms
Sergei Rachmaninoff
Gabriel Fauré
Modest Petrovich
Mussorgsky
Antonín Dvo:0159ák
Camille Saint-Saëns
The Coasters
Sergei Prokofiev
Paul Anka
Andrea Bocelli
Penguin Cafe
Orchestra
Luciano Pavarotti
Macaco
Glenn Miller
Sarah Brightman
Dmitri Shostakovich
Sir Edward
Elgar
Igor Stravinsky
Franz Joseph
Haydn
Johann Sebastian
Bach
Henry Purcell
Georg Friedrich
Händel
Georg Philipp
Telemann
Johann Pachelbel
Antonio Vivaldi
Tomaso Giovanni
Albinoni
Samuel Barber
Gustav Holst
Luigi Boccherini
Figure 6: The landscape of music
The Lemonheads
Giuseppe Verdi
ful Dead
Giacomo Puccini
Gioacchino Rossini
Georges Bizet
Johann Strauss
II
Richard Wagner
Richard Strauss
Hector Berlioz
Robert Schumann
Michael Nyman
Zbigniew Preisner
Erik Satie
Gabriel Fauré
Gustav Mahler
Kronos Quartet
Jean Sibelius
Maurice Ravel
Philip Glass
Modest Petrovich
Mussorgsky
Sergei Prokofiev
Igor Stravinsky
Penguin Cafe
Orchestra
Antonín Dvorák
Camille Saint-Saëns
Wolfgang Amadeus
Ludwig van
Mozart
Johann Sebastian
Beethoven
Felix Mendelssohn
Bach
Franz Joseph
Haydn
Georg Friedrich
Händel
Antonio Vivaldi
Sir Edward
Elgar
Dmitri Shostakovich
Franz Schubert
Johannes Brahms
Edvard Grieg
Sergei Rachmaninoff
Arvo Pärt
Frédéric Chopin
Pyotr Ilyich
Tchaikovsky
Franz Liszt
Claude Debussy
Georg Philipp
Telemann
Samuel Barber
Gustav Holst
Figure 7: Part of the classical archipelago
8
Henry Purcell
Johann Pachelbel
Luigi Boccherini
Tomaso Giovanni
Albinoni
Frankenstein cbeth The Good
Earth Night A Tale of Two
Cities Gertrude: A
Novel
Steppenwolf: A
Novel
Of Mice
& Men
The Journey to
the East
Twilight: The Complete Illustrated
Being And
Nothingness
The Ethics
Of Ambiguity
Being and
Time
Phenomenology of
Perception And Human
ns The Myth of Sisyphus:
And Other Essays
The Years
The Plague
The Adolescent
Resistance, Rebellion, and
Death: Essays
The Brothers
Karamazov
b's Room
The Double and
The Gambler
No Exit and Three
Other Plays
Spark Notes Crime
and Punishment
Chinua Achebe's Things Fall
Apart Welcome to the Monkey
The Soul and Barbed Wire:
An Introduction to Solzhenitsyn
Dead Souls:
A Novel
The Idiot
Demons
The Stranger
The Soviet Experiment: Russia, The U
and the Successor States
Journey into the
Whirlwind The Twelve
Chairs The Harvest of S
Collectivization and
Man Is Wolf to
Man: Surviving the Gulag
A Hero of Our
Time The Unbearable Lightness of
Being: A Novel
We
A Passage
to India
Chronicle of a
Death Foretold
The Remains of
the Day
Water for Elephants:
A Novel
The Th
A
On Chesil
Beach
Collected Stories
On Beauty
Home and
Dewey: The Small
Who Touche
Saturday
The English
Patient
The Autumn of the
Patriarch Of Love and Other
Demons Never Let
Me Go
Atone
The Memory
Keeper's Daughter
Snow Flower and the
Secret Fan: A Novel
Memories of My
Melancholy Whores
One Hundred Years of
Solitude Notes from
Underground
Suite Francaise
The Kite
Runner
A Heartbreaking Work of
Staggering Genius
Howards End
Stories of
Anton Chekhov
The Book of Laughter
and Forgetting
In the Skin of
a Lion
The Gulag Archipelago, 1918-1956: An Experiment
in Literary Investigation, Parts V-VII
The Gulag Archipelago
1918-1956 I-II
Gulag: A
History
The Gulag Archipelago 1918-1
Parts I - II
The Gulag Archipelago Volume 1: An
Experiment in Literary Investigation August 1914
The Unbearable Lightness of
Being War and
Peace The Gulag Archipelago 1918-1956 Abridged: An
Experiment in Literary Investigation In the First Circle:
A Novel A Thousand
Splendid Suns
Animal Farm
and 1984
All Quiet on the
Western Front
Home To
Harlem New Essays on Their Eyes
Rescuing Sprite: A Dog Lover's Story
of Joy and Anguish
The Gulag Archipelago: 1918-1956, An Experiment
in Literary Investigation III - IV
One Day in the Life
of Ivan Denisovich The Eternal Husband and
Other Stories
The Fall
Mrs. Dalloway
Cancer Ward
Heart of
a Dog
The Collected Tales of
Nikolai Gogol
The First
Man
The Blacker the
Berry Mother Night:
A Novel
We Never Make Mistakes:
Two Short Novels
The Solzhenitsyn Reader: New
and Essential Writings, 1947-2005
Introduction to
Metaphysics The Rebel: An Essay
on Man in Revolt
The Portable Harlem Rena
Reader Warning to
the West
The Master
and Margarita
Oblomov
Zo
Zora Neale Hurston's Their Eyes Were
Watching God: A Casebook Red Cavalry
Russiana
Eugene Onegin: A Novel
in Verse sea
Men in Black: How the Supreme
Court Is Destroying America
White Guard
A Room of
One's Own
The Fountainhead
Quicksand and
Passing The Invisible
Man Spark Notes
Invisible Man
Cat's Cradle:
A Novel
Breakfast of Champions:
A Novel
Solzhenitsyn: A Soul
in Exile
Basic Writings
Heidegger's Being And Time:
A Reader's Guide Steinbeck's the Grapes of
Wrath Tortilla Flat
Invisible Man
In Our
Time
Plum Bun: A Novel
Without a Moral
Their Eyes Were Watching
God Literature Guide Spark Notes The Grapes
of Wrath
Atlas Shrugged
A Dead Man's Memoir:
A Theatrical Novel Their Eyes Were
Watching God
Spark Notes The
Scarlet Letter
Spark Notes The
Great Gatsby
The Complete Short Stories of Ernest
Hemingway: The Finca Vigia Edition
The Forgotten Man: A New History
of the Great Depression
The Fatal
Eggs Fitzgerald's The Great
Gatsby For Whom the
Bell Tolls
Hawthorne's The Scarlet
Letter Death of a
Salesman Long Day's Journey
Capitalism and Freedom: Fortieth
Anniversary Edition
Beloved
Passing: The Things
They Carried
The Short Stories of F. Scott
Fitzgerald: A New Collection
A Farewell
To Arms
The Winter of Our
Discontent Personal Statement
The Daodejing
of Laozi
A Moveable
Feast
In Search of History Salem Witch Trials The Bri
of
Black Boy
Spark Notes The Adventures
of Huckleberry Finn
To Have and
Have Not
Interpre
Malad
The Bluest
Eye
Native Son
Their Eyes Were Watching
God CD
One Flew Over the
Cuckoo's Nest: Cannery Row:
The Glass
Menagerie
Moksha: Aldous Huxley's Classic Writings on
Psychedelics and the Visionary Experience
The Great
Gatsby
Slaughterhouse-Five: A
Novel
Islands in the Stream
: A Novel
The Grapes of
Wrath Travels with Charley in
Search of America: A Raisin in
the Sun
Huxley and
God: Essays
East of
Eden
The Old Man and
The Sea
The Adventures of
Huckleberry Finn
The Perennial
Philosophy The Rest Is Noise: Listening
to the Twentieth Century
Americana
The Scarlet
Letter
The Autobiography of Benjamin
Franklin nomenology of
Spirit The God of Small
Things: A Novel
Siddhartha
The Great
Gatsby CD
The Crucible
g Heights
The Ground Beneath Her
Feet: A Novel
The Sun
Also Rises
The Catcher in
the Rye
Island Narcissus and Goldmund:
A Novel
Narcissus and
Goldmund
Incidents in the Life of
a Slave Girl a Half of a
Yellow Sun
The Awakening
The Glass Bead
Game: Beneath the
Wheel
sion
God's Bits of
Wood Wide Sargasso Sea:
A Novel
A Clockwork
Orange
Of Mice and
Men Jane Eyre
f Two
nniversary The Moor's
Last Sigh
Midnight's Children:
A Novel
Nineteen Eighty-Four
To Kill
a Mockingbird
Mythology
A Man of
the People
War And
Peace Fahrenheit 451
Demian
Narrative of the Life
of Frederick Douglass Jane
No Longer
at Ease
Catch-22
Waiting for Godot: A
Tragicomedy in Two Acts
A Tree Grows in
Brooklyn Golding's the Lord of
the Flies Brave New World and
Brave New World Revisited
Brave New
World
1984
Animal Farm
Lord of the
Flies The Picture of Dorian
Gray - Oscar Wilde
Invisible
Man Chinua Achebe's Things Fall
Apart Wiesel's Night
Dr. Jekyll & Mr.
Hyde t Understanding Things Fall Apart:
Selected Essays and Criticisms
Chinua Achebe's Things Fall
Apart: A Casebook Huxley's Brave New
World Love in the Time
of Cholera The Glass Castle:
A Memoir
The History of Love:
A Novel
The Namesake:
A Novel
The Maytrees:
A Novel
Lov
A
The Gathering
Divisadero
Bridg
Figure 8: Two of the central clusters in BookLand.
neighborhood, there have been attempts to convey the underlining
connectivity of the products through graph visualization. None of
the existing approaches, however, gives a comprehensive view of
the relationship and the clustering structures.
same book, but with different publishers or different bindings, by
matching books with the same title. The underlying graph for this
map contains 913 vertices and 3410 edges. With an average degree
of nearly eight, peripheral vertices in this map have only a handful
of edges while central vertices have more than 20 immediate neighbors. We next examine several of the BookLand countries in more
detail (cf. Figures 8 and 1).
Using our GMap algorithm, we obtained the map in Figure 1. The underlying data is obtained with a breadth-first traversal following Amazon’s “Customers Who Bought This Item Also
Bought” links, starting from the root node, Orwell’s 1984. Links
are followed up to a distance of twelve from the root node. We
then trim the graph by keeping only vertices of distance nine or less
from the root vertex. We further merge nodes that represent the
Americana: Somewhat surprisingly, Orwell’s 1984 along with
Animal Farm ended up in the west corner of a region populated
mostly by American writers. Britain is also represented by Golding’s The Lord of the Flies and Huxley’s Brave New World along
9
partners.
with Burgess’s Clockwork Orange, which connect the British corner of the region to the main part dominated by 20th century American classics. Bradbury’s Fahrenheit 451 and Salinger’s Catcher
in the Rye provide a transition to a variety of well-known novels:
Steinbeck’s Grapes of Wrath and Of Mice and Men, Hemingway’s
For Whom the Bell Tolls, Fitzgerald’s Great Gatsby, Heller’s Catch22, and Kesey’s One Flew Over a Cuckoo’s Nest.
Russiana: To the north of Americana lies one of the largest
countries in BookLand, dominated by Russian literature and history. The core contains classic novels by Dostoyevsky (The Brothers Karamazov), Tolstoy (War and Peace), and Solzhenitsyn (The
Gulag Archipelago, Cancer Ward). In the west, there is, unexpectedly, a cluster of Camus books (The Stranger, The Plague, The
Fall), all well connected with the Russian classics.
From the map, we can also see some high-level connections.
There is the not too surprising proximity of self-help books with
books recommended by Oprah Winfrey. We also find the group of
recent vampire novels adjacent to the cluster of Victorian novels. A
closer investigation shows that vampire novels are only connected
to the rest of the graph through Jane Austen.
TVLand
As a last example, we consider a map derived from TV viewer data.
The objects are TV shows, with an edge between two shows if several viewers watched both. The edges are weighted by the number
of such viewers. We illustrate how GMap can be used in the context of recommendation systems with the aid of heat map overlays,
tailored to an individual viewer.
Figure 10 shows such a map for a typical but fictitious viewer.
Based on the observed viewing habits of a specific viewer, and on
those of other viewers, a recommendation system can suggest possible new shows. In order for the labels to be readable in a journal
format, our map displays a small subset of the TV shows and consists of six countries. The countries capture several types of shows
by genre. In the southwest are two countries corresponding to TV
shows for children, with the smaller targeting younger kids with
Dora the Explorer and Wow Wow Wubbzy. In the northwest is a
cluster of shows about fashion and entertainment such as Say Yes to
Dress and E! News. The large country in the north contains popular sitcoms such as Seinfeld, Cheers, and Frasier. In the northeast
is a cluster of crime shows (NCIS and Law & Order). The cluster
in the southeast is not as thematically focused but contains popular
shows of several types: The Oprah Winfrey Show, So You Think You
Can Dance, and Dateline NBC. In each country the TV shows are
colored based on the strength of the recommendation; that is, the
lighter the color, the stronger the recommendation, as in a typical
geographic map where shading of colors are used to represent hills
and valleys.
Maps like this provide a global view not available in the traditional list of recommendations and offer a more appealing presentation than a scatterplot or a node-link diagram. The map provides
a context for recommendations, allowing a user to understand the
reasoning behind the recommendation: when considering highlyrecommended shows the user can check out nearby, related shows;
or explore a path of shows to a new area.
TradeLand
Figure 9 is a map visualizing the trade relations between all countries. Bilateral trade data between each of the 209 countries
and its top trading partners were acquired from Mathematica’s
CountryData package. The font size of a label is proportional to
the logarithm of the total trade volume of the country, and the color
of a label reflects whether a country has a trade surplus (black) or
deficit (red).
The label color gives an easy way to spot the oil-rich countries
with large surpluses, which are distributed all over the world as
well as in our map: Middle East (Saudi Arabia, Kuwait), Europe
(Russia), South America (Venezuela), Africa (Nigeria, Equatorial
Guinea). On the other hand, the countries with huge deficits are
mostly in Africa (Sierra Leone, Senegal, Ethiopia) with the United
States, the clear outlier.
Many countries in close geographic proximity end up close in
our map, e.g, Central American countries like Honduras, El Salvador, Nicaragua, Guatemala and Costa Rica are close to each other
in the northeast. Similarly the three Baltic republics, Latvia, Lithuania and Estonia, are close to each other in the northwest. This is
easily explained by noting that geographically close countries tend
to trade with each other. There are easy-to-spot exceptions: North
Korea is not near South Korea, Israel is not particularly close to
Jordan or Syria.
The G8 countries (Canada, France, Germany, Italy, Japan, Russia, United Kingdom, and the United States) are all in close proximity to each other in the center of the map. Two of the largest
and closest countries in our map are China and the United States.
Clearly, the proximity is due to the very large trade volume rather
than geographic closeness. All these countries are in the largest
cluster which is dominated by European countries in the west,
Asian countries in the east, and Middle Eastern countries in the
south.
Interestingly, we see from the map that African countries are distributed in several clusters in close proximity to China (a major trading partner to many African countries), the United States (trading
less with Africa these days), and around former colonizers (e.g.,
Togo, Cameroon and Senegal, which are all close to France). On
the other hand, Caribbean and South and Central American countries form several clusters in the north of the map. In addition, these
clusters are mostly contiguous, essentially forming a supercluster.
This differentiation between Latin America and Africa is clearly
brought out by the GMap figure.
Finally, we note that the periphery of the map contains small
countries from around the world, and countries with few trading
R ELATED W ORK
There are many papers in geography about accurately and appealingly representing a given geographic region, or on re-drawing an
existing map subject to additional constraints. Examples of the first
kind of problem are found in traditional cartography, e.g., the 1569
Mercator projection of the sphere onto 2D Euclidean space. Cartograms provide an example of the second kind of problem, where
the goal is to redraw a map so that the geographical areas are proportional to some metric, an idea which dates back to 1934 and is
still popular today (e.g., the New York Times’ red-blue maps of the
US, showing the presidential election results in 2000 and 2004 with
states drawn proportional to population).
Work in information visualization has produced many ways of
representing data, some even adopting the name “map.” Most of
these have little visual connection with geographic maps. The map
of science [1] uses vertex coloring in a graph drawing to provide
an overview of the scientific landscape, based on citations of journal articles. Treemaps [11], squarified treemaps [2], and the more
recent newsmaps represent hierarchical information by means of
space-filling tilings, allocating area proportional to some metric.
Concept maps are diagrams showing relationships among concepts [10]. Somewhat similar are cognitive maps and mind-maps
used to represent words or ideas linked to and arranged around a
central key word.
In self-organizing maps (SOM) [8], an unsupervised learning algorithm places objects on a two-dimensional grid such that similar
objects are close to each other. Unlike GMap, SOM creates a “map”
by coloring cells of the grid based on a feature value; therefore it
10
Figure 9: A map of trade relations between countries.
operates on a discrete grid space without a clear inner boundary between “countries”. Furthermore, the grid tends to fit a rectangular
box, so that the overall outline of the point set often follows that
shape.
Also related is work on visualizing subsets of a set of items using geometric regions to indicate the grouping. Byelas and Telea [3]
use deformed convex hulls to highlight areas of interest in UML diagrams. Collins et al. [4] use “bubblesets,” based on isocontours,
to depict multiple relations among a set of objects. Simonetto et
al. [12] automatically generate Euler diagrams which provide one
of the standard ways, along with Venn diagrams, for visualizing
subset relationships. Apart from differences in the algorithms used
to generate regions, these works differ from ours in that they create regions that overlap with each other, with the goal of faithfully
representing set relationships, while we take the map metaphor seriously, assuming that regions do not overlap and aiming for a cartographic verisimilitude. However, as Figure 5 shows, our approach
can also be used for visualizing sets.
Representing imagined places on a map as if they were real countries also has a long history, e.g., the 1930’s Map of Middle Earth
by Tolkien and the Bücherlandes map by Woelfle from the same period. More recent drawings include maps of programming language
concepts and online communities. While most such maps are generated in an ad hoc manner by hand and are not strictly based on
underlying data, they are often visually appealing.
Generating synthetic geography has a large literature, connected
to its use in computer games and movies. Most of the work relies
on variations of a fractal model. These techniques could provide
additional photo-realism, and may be used in future extension of
our work.
C ONCLUSION AND F UTURE W ORK
We believe that the GMap algorithmic framework, by capitalizing
on an ancient and familiar visual metaphor, introduces a significant new style of viewing abstract relational and cluster data that
users will find aesthetically appealing and helpful in understanding
the data. From informal observation, we have found that people,
when faced with a traditional graph drawing and a map of the same
data, spend significantly longer time studying the map, and they
find non-trivial structural information without any prompting. For
example, several viewers of BookLand observed that the “gateway”
to Fringistan is Rand’s Atlas Shrugged. We plan to perform formal
user studies of the interaction with graphs and maps, in the context
of visualizing recommendations [6].
While the approach of visualizing relational information with the
aid of geographical maps is general, here we showed one particular implementation, where embedding and clustering algorithms
are coupled with novel mapping and coloring algorithms. There
are specific idiosyncrasies we would like to address. For example,
GMap can produce countries that are disconnected. We have proposed ways [5] to use the cluster information to adjust the layout so
that the regions of countries are more contiguous, at the expense of
some loss of graph information. We are planning further investigation into balancing country connectedness and the preservation of
11
Figure 10: Personalized recommendation heat map for a typical user. Regions where the highest recommended shows sit are lighter. Regions of low scores are
darker.
graph information.
Finally, our algorithm is efficient and can handle large graphs.
As a reference point, all maps in this paper were generated in a few
seconds. Mapping a larger graph with 440, 000 vertices took 4 minutes on a typical processor. The resulting maps look best on large,
wall-sized posters and display walls. To make such maps more useful for exploration of large data sets on commonly available media,
we have developed an interactive interface that can search, zoom
and pan easily on the maps (see footnote 1).
[6] E. R. Gansner, Y. F. Hu, S. G. Kobourov, and C. Volinsky. Putting
recommendations on the map - visualizing clusters and relations. In
Proceedings of the 3rd ACM Conference on Recommender Systems.
ACM, 2009.
[7] A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many:
Illumination cone models for face recognition under variable lighting
and pose. IEEE Trans. Pattern Anal. Mach. Intelligence, 23(6):643–
660, 2001.
[8] T. Kohonen. Self-Organizing Maps. Springer, 2000.
[9] M. E. J. Newman. Modularity and community structure in networks.
Proc. Natl. Acad. Sci. USA, 103:8577–8582, 2006.
[10] J. D. Novak and A. J. Cañas. The theory underlying concept maps
and how to construct them. Technical report, Institute for Human and
Machine Cognition, January 2006.
[11] B. Shneiderman. Tree visualization with tree-maps: A 2-D spacefilling approach. ACM Transactions on Graphics, 11(1):92–99, 1992.
[12] P. Simonetto, D. Auber, and D. Archambault. Fully automatic visualisation of overlapping sets. Computer Graphics Forum, 28:967–974,
2009.
Acknowledgments
We would like to thank Stephen North for helpful discussions, and
Carlos Scheidegger for help with the image process example.
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