Visualizing Networks: Beyond the Hairball

Visualizing Networks
Beyond the “Hairball”

Lynn Cherny
@arnicas

O’Reilly Strata NYC 2012

PS(A): I AM NOT JASON
SUNDRAM
He could not make it, and asked me to take over.

2

The Hairball: A Metaphor for
Complexity

http://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientiﬁc%20Ameri
%20288,%2060-69%20(2003).pdf

http://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientiﬁc%20Ameri
%20288,%2060-69%20(2003).pdf

http://www.linkedin.com/today/post/article/20121016185655-10842349-the-hidden-power-

WHAT IS A NETWORK?

It’s not a visualization. Think of it as a data structure.

Data relationship: entities + relationships
to other objects (node/edge, vertex/link)

Nodes and Edges may have attributes, eg.
gender, age, weight, tv prefs
connection date, frequency of contact, type of
exchange, directionality of relationship
attributes may be calculated from network itself

Lane Harrison: http://blog.visual.ly/network-visualizations/

Best!

A User Study on Visualizing Directed Edges in Graphs”
Danny Holten and Jarke J. van Wijk, 27th SIGCHI Conference on Human Factors in
Computing Systems (Proceedings of CHI 2009),

http://blog.visual.ly/network-visualizations/ 9

It’s a natural human trait to see visual similarity and proximity as
meaningful.

Be very careful about your display choices and layout methods!

10

Reading a network visualization

There’s obviously
something important
going on here,
structurally....

Lo o
k a
t th
is o
utl
ier
cas
e!

There’s obviously
something important
going on here,
structurally....

à Lo o
age k a
mé
n ere t th
A ver h is o
i s o utl
tro ier
cas
e!

There’s obviously
something important
going on here,
structurally....

S?
MIReading a network visualization
à Lo o
age k a
mé
n ere t th
A ver h is o
i s o utl
tro ier
cas
e!

There’s obviously
something important
going on here,
structurally....

Using a “random” Gephi layout on the dolphins

S?
MIReading a network visualization
à Lo o
age k a
mé
n ere t th
A ver h is o
i s o utl
tro ier
cas
e!

Rando m!

There’s obviously
something important
going on here,
structurally....

Using a “random” Gephi layout on the dolphins

Design Examples s!
lp h in
o
it h D
w

The Dolphins of Doubtful Sound

http://www.doc.govt.nz/documents/conservation/native-animals/marine-mammals/abundance-population-
structure-bottlenose-dolphins-doubtful-dusky-sounds.pdf

“The bottlenose dolphin community of Doubtful
Sound features a large proportion of long-lasting
associations. Can geographic isolation explain this
unique trait?”

David Lusseau et al. BEHAVIORAL ECOLOGY AND
SOCIOBIOLOGY Volume 54, Number 4 (2003)
http://www.springerlink.com/content/pepxvj4lu42ur2gw/

“The bottlenose dolphin community of Doubtful
Sound features a large proportion of long-lasting
associations. Can geographic isolation explain this
unique trait?”

!
ti tle
p er
l pa
tua
ac
e
Th

David Lusseau et al. BEHAVIORAL ECOLOGY AND
SOCIOBIOLOGY Volume 54, Number 4 (2003)
http://www.springerlink.com/content/pepxvj4lu42ur2gw/

“SF”

“ULT”

2 hung
out

D. Lusseau, Evidence for Social Role in a Dolphin Social Network.
Evol Ecol (2007) 21:357–366

Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful Sound Bottlenose Dolphin Population?. PLoS ONE 2(4):
e348. doi:10.1371/journal.pone.0000348


deﬁne relationship
Using “mirroring” to


deﬁne edges
“headbutting” to
deﬁne relationship
UsingUsing “mirroring” to

TOOLS FOR TODAY

Creating network layouts...

17

Or “D3” (d3js.org)

• A “build it yourself” svg-based visualization
library
• Import graphs as (or parse to create) a json
node-link structure

Making a Network
Who is your audience? What’s the goal?
Exploration / Iterative visualization during data analysis?
End-user communication?

Making a Network
Who is your audience? What’s the goal?
Exploration / Iterative visualization during data analysis?
End-user communication?

Layout choices: by hand, algorithmic, style...
Understand the global and local context with some stats about
actors and roles in the network
Improve your layout with stats / attributes - inherent (such as
gender) or calculated (e.g., degree)
Add interactivity for end users if appropriate

J Bertin: Semiology
of Graphics
Linear

Circular

Irregular

Regular (Tree)

3D

Matrix /
Bipartite

Bertin, J. Semiology of Graphics: Diagrams,
Networks, Maps (1967)

Algorithmic Approaches

Frank van Ham talk slides: http://bit.ly/s6udpy

(Trees are a whole other subject)

Treevis.net: http://www.informatik.uni-rostock.de/~hs162/treeposter/poster.html

MATRIX LAYOUTS /
REPRESENTATIONS

http://barabasilab.neu.edu/networksciencebook/download/network_science_October_2012.pdf

Real social networks are
generally quite sparse.

http://www.cise.uﬂ.edu/research/sparse/matrices/Newman/
dolphins.html

D3 demo by me http://www.ghostweather.com/essays/talks/networkx/adjacency.html

NodeTrix: A Hybrid Visualization of Social Networks. Nathalie Henry, Jean-Daniel Fekete, and
Michael J. McGufﬁn. (2007) http://arxiv.org/abs/0705.0599

http://semilattice.net/eyeodiff/

Philipp Steinweber and Andreas Koller
Similar Diversity, 2007

For a D3 example in another domain: http://tradearc.laserdeathstehr.com/

http://www.openbible.info/blog/2010/04/bible-cross-references-visualization/

Hive Plots

D3: http://bost.ocks.org/mike/hive/

http://mariandoerk.de/pivotpaths/

Bertin’s Thought Process

Bertin, J. Semiology of Graphics: Diagrams, Networks, Maps (1967)

(P) Paris

(Z) Paris Suburbs

(+50) Communes of >50K

(+10) Communes of >10K

(-10) Communes of <10K

(R) Rural

“If
it's
Circos pro
bab
rou
ly d
nd,
o it
Circ
os
” can

http://circos.ca/images/

Simple Orderings of Nodes

Circular Layout “Dual Circle” layout with Sorted by
ordered by Degree most popular dolphin in center Modularity

41
Dolphins colored by modularity class (community) in Gephi

http://www.ghostweather.com/essays/talks/networkx/chord.html

Hierarchical Edge Bundling

"Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data”, Danny Holten, IEEE
Transactions on Visualization and Computer Graphics (TVCG; Proceedings of Vis/InfoVis 2006), Vol. 12, No. 5,

A D3 Example by M. Bostock

D3: http://bl.ocks.org/1044242

A very short detour into
maps...

46
Flow Map Layout, Phan et al (2005) http://graphics.stanford.edu/papers/ﬂow_map_layout/

"Force-Directed Edge Bundling for Graph Visualization”,
Danny Holten and Jarke J. van Wijk, 11th Eurographics/IEEE-VGTC Symposium on Visualization (Computer
Graphics Forum; Proceedings of EuroVis 2009), Pages 983 - 990, 2009.

Divided Edge Bundling for Directional Network Data
David Selassie, Brandon Heller, Jeffrey Heer
IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2011 48

50
http://jeromecukier.net/projects/agot/places.html

51
Jerome’s version with the map is not available online, sorry.

Yet Another Design
Example

Moritz Stefaner’s Muesli Problem

https://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected

s
li ke iou
ch lic
u
m ot de
o
“ To s, n h!” el
f
o om oug ims
hr en to h
us
m i tz
or
-M


Final


ALGORITHMIC LAYOUTS
Gephi / D3.js / Other tools

Gephi  Sigma.js
Gephi.org: Open source, runs on Mac, Linux, PC
Can be run from a python-esque console plugin or UI
Can be run “headless” for layouts (Jason Sundram)
https://github.com/jsundram/pygephi
Plugins include a Neo4j graph db access, and streaming
support
Sigma.js :
Will display a gexf gephi layout ﬁle with minimal work, using
a plugin interpreter for sigma
Also offers a force-directed layout plugin for graphs without
x&y coords
Does CANVAS drawing, not SVG

http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200705-14_PNAS-HumanDisease/Suppl/

Movie:

Sigma.js version of the Gephi export http://exploringdata.github.com/vis/human-disease-

Using Sigma.js
basic_sigma.js

<div class="sigma-expand“ function init() {
// Instanciate sigma.js and customize rendering :

id="sigma-example"></div> var sigInst = sigma.init(document.getElementById('sigma-example'))

.drawingProperties({
defaultLabelColor: '#fff',
defaultLabelSize: 14,
defaultLabelBGColor: '#fff',
defaultLabelHoverColor: '#000',
labelThreshold: 6,
defaultEdgeType: 'curve'
}).graphProperties({
minNodeSize: 0.5,
maxNodeSize: 5,
minEdgeSize: 1,
maxEdgeSize: 1
}).mouseProperties({ Where
maxRatio: 32

}); to put
// Parse a GEXF encoded ﬁle to ﬁll the graph the
<script src="../js/sigma.min.js"></script> // (requires "sigma.parseGexf.js" to be included)
<script src="../js/sigma.parseGexf.js"></script> sigInst.parseGexf('color_by_mod.gexf'); graph
<script src="basic_sigma.js"></script> // Draw the graph :
sigInst.draw();
}
Your graph
if (document.addEventListener) {
document.addEventListener("DOMContentLoaded", init, false);
} else {
window.onload = init;
}

In sigma.js’s github (under plugins!)

Sample Layout Plugins in Gephi

https://gephi.org/tutorials/gephi-tutorial-layouts.pdf

Gephi Plugin Layout Details
Layout Complexity Graph Size Author Comment

Circular O(N) 1 to 1M nodes Matt Groeninger Used to show
distribution, ordered
layout

Radial Axis O(N) 1 to 1M nodes Matt Groeninger Show ordered groups
(homophily)

Force Atlas O(N²) 1 to 10K nodes Mathieu Jacomy Slow, but uses edge
weight and few
biases
Force Atlas 2 O(N*log(N)) 1 to 1M nodes Mathieu (does not use
Jacomy weight)

OpenOrd O(N*log(N)) 100 to 1M nodes S. Martin, W. M. Focus on clustering
Brown, R. Klavans, (uses edge weight)
Yifan Hu O(N*log(N)) 100 to 100K nodes and K. Boyack
Yifan Hu (no edge weight)

Fruchterman- O(N²) 1 to 1K nodes Fruchterman & Particle system, slow
Rheingold Rheingold! (no edge weight)
GeoLayout O(N) 1 to 1M nodes Alexis Jacomy Uses Lat/Long for
layout

https://gephi.org/2011/new-tutorial-layouts-in-gephi/

Dolphins Again

OpenOrd + “No Overlap” ForceAtlas2

63

Unweighted
dolphins,
Force Atlas

Weight 2: Force Atlas Weight 4: Force Atlas
64

Unweighted
dolphins,
Force Atlas

Weight 2: Force Atlas Weight 4: Force Atlas Weight 4: Yifan Hu
64

Canvas/SVG benchmarks from the
d3.js group:

https://docs.google.com/spreadsheet/
ccc?
Nick Diakapolous: http://nad.webfactional.com/ntap/graphscale/ key=0AtvlFoSBUC5kdEZJNVFySG9wSHZk

SIMPLE CALCULATIONS ON
NETWORKS CAN TELL YOU
Often you need to visualize the structure/role of the graph
elements as part of the visualization: So, do some simple
math.

Degree (In, Out)
“Degree” is a measure of the
edges in (directed), out
(directed), or total (in
directed or undirected
graphs) to a node
“Hub” nodes have high in-
degree. In scale-free
networks, we see preferential
attachment to the popular
kids.

http://mlg.ucd.ie/ﬁles/summer/tutorial.pdf

Scale-free Networks

Image from Lada Adamic’s SNA Course on Coursera pdf 3D

The Threat of Hub-Loss

Albert-László Barabási and Eric Bonabeau, Scale-Free Networks, 2003.http://www.scientiﬁcamerican.com/
article.cfm?id=scale-free-networks

Visualization Aside: If Some Names are
Huge, the Others are Invisible-?

70
Correcting for text size by degree display issue

Visualization Aside: If Some Names are
Huge, the Others are Invisible-?

Gephi Panel

70
Correcting for text size by degree display issue

Betweenness
A measure of connectedness
between (sub)components of
the graph

“Betweenness centrality thus
tends to pick out boundary
individuals who play the role of
brokers between communities.”

Lusseau and Newman. http://www.ncbi.nlm.nih.gov/pmc/
articles/PMC1810112/pdf/15801609.pdf http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Judging By Eye Will Probably Be
Wrong...

72

Wrong...

? This one?

72

Wrong...

? This one?

Sized by Betweenness

72

Eigenvector Centrality
Intuition: A node is important if it is
connected to other important nodes
A node with a small number of inﬂuential
contacts may outrank one with a larger
number of mediocre contacts

http://mlg.ucd.ie/ﬁles/summer/tutorial.pdf http://demonstrations.wolfram.com/

Community Detection
E.g., the Louvain method, in Gephi as
“Modularity.” Many layout algorithms help
you intuit these structures, but don’t rely
on perception of layout!

http://en.wikipedia.org/wiki/File:Network_Community_Structure.png

Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful Sound
Bottlenose Dolphin Population?. PLoS ONE 2(4): e348. doi:10.1371/journal.pone.0000348

Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful Sound
Bottlenose Dolphin Population?. PLoS ONE 2(4): e348. doi:10.1371/journal.pone.0000348 77

Identifying the role that animals play in their social networks (2004)
D Lusseau, MEJ Newman
Proceedings of the Royal Society of London. Series B: Biological Sciences

Eduarda Mendes Rodrigues, Natasa Milic-Frayling, Marc Smith, Ben Shneiderman, Derek Hansen, Group-in-a-
box Layout for Multi-faceted Analysis of Communities. IEEE Third International Conference on Social

http://mbostock.github.com/d3/talk/20111116/force-collapsible.html

Movie:

Ger Hobbelt D3: http://bl.ocks.org/3616279

100 nodes, size by degree, Clustered by partition, no edges
shaded by Betweenness, in until you click on one, node size is a
d3.js force directed layout. choice of attributes, nodes
represented by labels/colors….

http://www.ghostweather.com/essays/talks/networkx/ http://www.ghostweather.com/essays/talks/networkx/

Movie:

84
http://www.ghostweather.com/essays/talks/networkx/force_fonts.html

Movie:

by @moebio
85
http://intuitionanalytics.com/pleiades/

!
LOL

SO YOU WANT TO LAY IT OUT
YOURSELF...
Perfectionist? Artist? Don’t like algorithms?

86

Jeff Heer: http://hci.stanford.edu/courses/cs448b/f11/lectures/CS448B-20111110-

Conspiracy Theorist Mark

Learning from Lombardi:
http://benfry.com/exd09/

Stefanie Posavec

http://www.itsbeenreal.co.uk/index.php?/wwwords/literary-

Roche Applied Science Biochemical Pathways Map: http://web.expasy.org/cgi-bin/pathways/

Hybrid Method: Use algorithmic
layout, and then adjust nodes by

Ger Hobbelt in D3: http://bl.ocks.org/3637711

Tweaking your layout is
addictive!

Yo ned
wa

u
r
ha !
ve
!

be
en
Jason Davies: http://www.jasondavies.com/planarity/

STEP BACK, SCALE UP...

94

C.Dunne & B.Shneiderman. Network Motif Simpliﬁcation. http://hcil2.cs.umd.edu/trs/

GraphPrism: Compact Visualization of Network Structure
Sanjay Kairam, Diana MacLean, Manolis Savva, Jeffrey Heer
Advanced Visual Interfaces, 2012

97
http://openaccess.city.ac.uk/1324/

Video:

98
http://graphics.wsj.com/political-moneyball/

Reminders
Choose your visual encodings, layout,
interaction to make it a visualization,
rather than raw data vomit.
Take care: people will infer things from
proximity/similarity even if it was not
intended!
Do data analysis / reduction - why
would you want to show 1T of network
data?
Allow interactivity if needed for end
users.
Help people ﬁnd things in your network!

Reminders
Choose your visual encodings, layout,
interaction to make it a visualization,
rather than raw data vomit.
Take care: people will infer things from

To ot sci
proximity/similarity even if it was not

=N

o
m wa ce
d
intended!

at

uc
al en
a

h
d go
Do data analysis / reduction - why

at
ys !

a
would you want to show 1T of network

o
d
data?
Allow interactivity if needed for end
users.
Help people ﬁnd things in your network!

More Reminders!
Different layouts communicate different
things to your viewer - choose wisely
Reducing noise:
Don’t show edges (perhaps on demand)
Show details only on demand: zoom in
Cluster your nodes/edges
Consider if it has to be a “network” display at all: Is
it the stats you care about? Or the hairball?

101

The Map is Not the Territory…

Forest Pitts (thanks to Noah Friedkin) http://www.analytictech.com/networks/pitts.htm

Thanks!
@arnicas
lynn@ghostweather.com

And thanks to twitter vis friends for content: @jsundram, @laneharrison, @moritz_stefaner, @jcukier,
@jeff1024, @vlandham, @moebio, @jeffrey_heer, @mbostock, @eagereyes, @jasondavies, @stefpos,
@sarahslo, @ndiakopoulos, @gephi

A Few More References
Jeff Heer class slides: http:// Robert Kosara’s post: http://
hci.stanford.edu/courses/cs448b/ eagereyes.org/techniques/graphs-
w09/lectures/20090204- hairball
GraphsAndTrees.pdf
Lane Harrison’s post: http://
A great in-progress book on networks: blog.visual.ly/network-
http://barabasilab.neu.edu/ visualizations/
networksciencebook/
MS Lima’s book Visual Complexity
Mark Newman’s many papers: http://
Jason Sundram’s tool to drive Gephi
www-personal.umich.edu/~mejn/
layout from command line: https://
Eyeo Festival videos from Moritz github.com/jsundram/pygephi
Stefaner, Manuel Lima, Stefanie
A couple articles on community
Posavec
structure:
Journal of Graph Algorithms and
Overlapping Community Detection
Applications: http://jgaa.info/
in Networks: State of the Art
home.html
and Comparative Study by Jierui
Jim Vallandingham’s D3 network Xie, Stephen Kelley, Boleslaw K.
tutorials: http://ﬂowingdata.com/ Szymanski
2012/08/02/how-to-make-an-
Empirical Comparison of
interactive-network-visualization/, Algorithms for Network

Visualizing Networks: Beyond the Hairball

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Visualizing Networks: Beyond the Hairball

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