Basic network measures and visualization

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Unit 3: Basic Visualization
ICPSR
University of Michigan, Ann Arbor
Summer 2016
Instructor: Ann McCranie
Compare three sociograms with
exactly the same density….
N=50
N=100
N=500
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Compare two more, same
density, but one is undirected and
the other is not….
Both N=50
“A process of charting has been devised by the sociometrists, the
sociogram, which is more that merely a method of presentation. It is first
of all a method of exploration. It makes possible the exploration of
sociometric facts . . . It is at present the only available scheme which
makes structural analysis of a community possible.”
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J.L. Moreno. 1934. “Who Shall Survive? Foundations of Sociometry, Group
Psychotherapy and Sociodrama.” Beacon, NY: Beacon House, Inc. (Quote
from pp. 95-96 and via Lin Freeman’s work on visualization and social
networks: http://moreno.ss.uci.edu/80.pdf
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What makes a good network
visualization?
•  It depends.
•  Do you want help “telling your story?”
•  Do you want to use visualization as a way
to explore the structure of your networks?
•  Are you trying to highlight individuals or
structure?
•  What are you trying to convey?
This tells a powerful story…
Bearman PS, Moody J, Stovel K. "Chains of affection: The
structure of adolescent romantic and sexual networks" American
Journal of Sociology, Vol. 100, No. 1.
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Sometimes sociograms can truly
be “maps” you can explore
From the fantastic “Maps of Science” at
http://mapofscience.com/
Sometimes they reveal subgroups
J.K. Harris, K.E. Beatty, J.D. Lecy, J.M. Cyr, R.M. Shapiro II
Mapping the multidisciplinary field of public health services and systems research
Am J Prev Med, 41 (1) (2011), pp. 105–111
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Things you must consider!
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Size of your network
Nature of ties (directed? Weighted?)
Density of your ties.
What attribute information you need to
show and what type of variables those
are (categorical, continuous, etc)
•  Your publication/presentation outlet
Basic elements for sociogram
•  Node – size, color, shape
•  Sometimes people color “rims” of nodes to denote
additional information
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Labels – size, color, shape
Ties – size, color and shape
Arrows (showing directionality)
Legend (for all those colors, sizes and
shapes!)
•  Layout – hand-drawn, algorithm, etc.
•  Description to accompany it
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Description to accompany your
sociogram – what I recommend
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The data source
The size of the network
The density of ties
The meaning of sizes, colors, and
shapes (unless clear in a legend)
•  The layout – hand-drawn, layout
algorithm
•  The software package you used
The “spring embedder” and
“forced-based layout” algorithms
•  Treat nodes and lines as if they are part of a
spring-based or mechanical system.
•  These end up putting nodes with more shared ties
closer together and moving others away
•  There are many solutions to how to
accomplish this.
•  Popular ones are Fruchterman-Reingold,
Kamada-Kawaii, and spring embedded.
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Multidimensional Scaling
Approaches
•  These are approaches that try to pair nodes closer in
a two dimensional space together based on how
“similar” they are to one another.
•  Each solution solves similarity in a different way.
•  Netdraw offers solutions based on:
•  Multidimensional scaling (in this case, they are using nonmetric MDS)
•  Principal components analysis
•  Gower Metric Scaling
•  You can then move nodes and lines around using
other options (edge length bias and node repulsion,
for instance)
HOSPCONS, Netdraw, Clockwise from left: Gower metric scaling, MDS, PC, circle
with optimization, spring embedding with geodesic distances + node repulsion +
equal edge length bias, random
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Suggestions for better
visualizations…
•  Simpler is usually better. Instead of
cramming a lot of information into one
image, create a panel of different
visualizations using the same layout so
you can compare.
•  As you teach yourself how to look at
graphs, you might want to pick a
standard layout algorithm that you use
so you become accustomed to it.
More suggestions…
•  I often make my ties a lighter gray so
they do not overwhelm my image
•  Anytime you have an undirected
relation, DROP THE ARROWS.
•  Sometimes I only label key actors in
large networks
•  Use software (Pajek, Visone, Network
Workbench, statnet) where you can
create SVG files and edit them.
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Figure 1: Largest connected component of network of Recovery researchers,
from ISI Web of Science citation data, 1991-2008, N=891.
Note: The colors represent different CONCOR groups discovered in an initial
analysis. Only key actors are labeled. The size of the nodes reflects the number
of articles published during that time period. Layout is Fruchterman-Reingold
in a square space in Pajek, which results in the characteristic round shape.
Further modification was in image software to prepare for publication.
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Comparisons
•  Perhaps you have multiplex relations, or
you have networks that evolve over
time. For print, you can choose panels.
You can also produce dynamic network
visualization.
•  See:
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“Dynamic Network Visualization: Methods for Meaning with Longitudinal Network
Movies” American Journal of Sociology 110:1206-1241
SoNIA: http://www.stanford.edu/group/sonia/
Here follows a network that
changes over time
Co-authorship of Recovery Researchers
from 1991-2008. N=891. Only key actors
are labeled. The size of the nodes reflects
the number of articles published during
that time period. Layout is FruchtermanReingold in a square space in Pajek,
which results in the characteristic round
shape. Further modification was in image
software to prepare for publication.
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1991-1995
1996-2000
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2001-2005
2006-2008
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1991-2008
Modularity in the
genetic diseasephenotype network
FEBS Letters,
Volume 582, Issue
17, Pages 2549-2554
Xingpeng Jiang,
Bing Liu, Jiefeng
Jiang, Huizhi Zhao,
Ming Fan, Jing
Zhang, Zhenjie Fan,
Tianzi Jiang
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Hidalgo, Klinger, Barabasi, Hausmann. 2007. http://scimaps.org/maps/map/
the_product_space_5/
It's a bird, it's a mosquito, it's a pig!
The Spread of Pandemic Flu
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http://well-formed.eigenfactor.org/index.html
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Exemplary visualizations and
resources
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Butts, Carter T. (2009). ``Revisiting the Foundations of Network Analysis.''
Science, 325, 414-416.
Kwang-Il Goh, Michael E. Cusick, David Valle, Barton Childs, Marc Vidal,and
Albert-László Barabási. The human disease network PNAS 2007 104 (21)
8685-8690
Pablo Kaluza, Andrea Kölzsch, Michael T. Gastner, and Bernd Blasius. The
complex network of global cargo ship movements J. R. Soc. Interface July 6,
2010 7:1093-1103
W. Van den Broeck, C. Gioannini, B. Goncalves, M. Quaggiotto, V. Colizza, A.
Vespignani The GLEaMviz computational tool, a publicly available software to
explore realistic epidemic spreading scenarios at the global scale. BMC
Infectious Diseases, 11 :37 (2011)
Krempel, Lothar. Network Visualization. 2011 The SAGE Handbook of Network
of Social Network Analysis
•  Chapter 7 – Borgatti, Everett, and Johnson (2013)
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