Interactive visualization and exploration
of network data with gephi
Bernhard Rieder
Universiteit van Amsterdam
Mediastudies Department
and some conceptual context

Context
Terms like "big data", "computational social science", "digital humanities",
"digital methods", etc. are receiving a lot of attention.
They point to a set of practices of knowledge production: data analysis,
visualization, modeling, etc.
Instead of a totalizing search for a "logic" of data analysis, we could
inquire into the vocabulary of concepts and analytical gestures that
constitute the practice of data analysis.
A twofold approach to methods:
☉ Engagement, development, application => digital methods
☉ Conceptual, historical, and political analysis and critique => software studies

This workshop
How do we talk about data? How do we analyze them? What is our frame
of thought? How do we go further in terms of imagination, expressivity?
☉ Introduction
☉ A bit of math
☉ Two kinds of mathematics
☉ Concepts and techniques from graph theory
☉ Working with gephi
Engage the theory of knowledge (epistemology) mobilized in data analysis,
but through the actual techniques and not generalizing concepts.

Basic ideas
Why?
Why do network analysis and visualization? Which arguments are put
forward?
☉ New media: technical and conceptual structures modeled as networks
☉ Calculative capacities: powerful techniques and tools
☉ Visualization: the network diagram, "visual analytics"
☉ Logistics: data and software are available
☉ Methodology: dissatisfaction with statistics (SNA)
☉ Society: diversification, problems with demographics / statistics / theory

Platforms like Twitter
boost opportunities for
connectivity between
various types of actors.

At the same time, they
produce detailed data
traces that are highly
centralized and searchable.
Much of these data can be
analyzed as graphs.

What styles of reasoning?
Hacking (1991) building the concept of "style of reasoning" on A. C.
Crombie’s (1994) "styles of scientific thinking":
☉ postulation and deduction
☉ experiment and empirical research
☉ reasoning by analogy
☉ ordering by comparison and taxonomy
☉ statistical analysis of regularities and probabilities
☉ genetic development
What kind of reasoning are we mobilizing in data analysis?
Is it one type of reasoning or many?
Are we "positivists" when we do data analysis? Reductionists?

Quality / quantity
"One of my favorite fantasies is a dialogue between Mills and Lazarsfeld in which the former
reads to the latter the first sentence of The Sociological Imagination: 'Nowadays men often
feel that their private lives are a series of traps.' Lazarsfeld immediately replies: 'How many
men, which men, how long have they felt this way, which aspects of their private lives
bother them, do their public lives bother them, when do they feel free rather than trapped,
what kinds of traps do they experience, etc., etc., etc.' If Mills succumbed, the two of them
would have to apply to the National Institute of Mental Health for a million-dollar grant to
check out and elaborate that first sentence. They would need a staff of hundreds, and when
finished they would have written Americans View Their Mental Health rather than The
Sociological Imagination, provided that they finished at all, and provided that either of them
cared enough at the end to bother writing anything." (Maurice Stein, cit. in Gitlin 1978)
Theory vs. empiricism, macro vs. micro, qualitative vs. quantitative, inductive vs.
deductive, associative vs. formalistic, etc.
The promise of data analysis tools, applied to exhaustive (and cheap) data, is to
bridge the gap, to allow zooming, "quali-quanti" (Latour 2010).

Two kinds of mathematics
Can there be data analysis without math? No.
Does this imply epistemological commitments? Yes.
But there are choices, e.g. between:
☉ Confirmatory data analysis => deductive
☉ Exploratory data analysis (Tukey 1962) => inductive
There is a fast growing variety of formal analytical gestures relying on
mathematical modeling and computation.

Two kinds of mathematics
Statistics
Observed: objects and properties
Inferred: social forces
Data representation: the table
Visual representation: quantity charts
Grouping: "class" (similar properties)
Graph-theory
Observed: objects and relations
Inferred: structure
Data representation: the matrix
Visual representation: network diagrams
Grouping: "clique" (dense relations)

Graph theory
Leonhard Euler, "Seven Bridges of Königsberg", 1735
Introducing the "point and line" model

Graph theory
Develops over the 20th century, in particular the second half.
Integrates branches of mathematics (topology, geometry, statistics, etc.).
Graph theory is "the mathematics of structure" (Harary 1965), "a
mathematical model for any system involving a binary relation" (Harary
1969); it makes relational structure calculable.
"Perhaps even more than to the contact between mankind and nature, graph theory owes to
the contact of human beings between each other." (König 1936)

Basic ideas
Moreno 1934
Graph theory developed in
exchange with sociometry,
small-group research and
(later) social exchange
theory.
Starting point:
"the sociometric test"
(experimental definition of
"relation")

Basic ideas

Forsythe and Katz, 1946, "adjacency matrix"

Harary, Graph Theory, 1969

Basic ideas
The late 1990s
The network "singularity":
☉ The network imaginary, a "new science of networks" (Watts 2005)
☉ Computational capacities (memory, speed, interfaces, etc.)
☉ New platforms and datasets
☉ Packaged tools
Different traditions conflate to form network analysis:
☉ Social network analysis and sociometrics
☉ Scientometrics / science and technology studies
☉ Mathematics / physics / computer science
☉ Information and data visualization
☉ Digital sociology / new media studies

Basic ideas
Adamic and Glance, "Divided They Blog", 2005

Formalization
"As we have seen, the basic terms of digraph theory are point and line. Thus, if an
appropriate coordination is made so that each entity of an empirical system is identified
with a point and each relationship is identified with a line, then for all true statements
about structural properties of the obtained digraph there are corresponding true statements
about structural properties of the empirical system." (Harary et al. 1965)
There is always an epistemological commitment!
=> What can "carry" the reductionism and formalization?
=> What types of analytical gestures?

Facebook Page "ElShaheeed", June 2010 – June 2011, (Poell / Rieder, forthcoming)
7K posts, 700K users, 3.6M comments, 10M likes (tool: netvizz), work in progress!

Facebook Page "ElShaheeed", June 2010 – June 2011:
comment timescatter, log10 y scale, likes on

Facebook Page "ElShaheeed", June 2010 – June 2011:
scatterplot comments / likes, per post type

Facebook Page "ElShaheeed"
700K nodes, 11M connections
Color: type

Facebook Page "ElShaheeed"
700K nodes, 11M connections
Color: outdegree

Basic ideas
What Kind of Phenomena/Data?
Interactive networks (Watts 2004): link encodes tangible interaction
☉ social network
☉ citation networks
☉ hypertext networks
Symbolic networks (Watts 2004): link is conceptual
☉ co-presence (Tracker Tracker, IMDB, etc.)
☉ co-word
☉ any kind of "structure" that can be as point and line
=> do all kinds of analysis (SNA, transportation, text mining, etc.)
=> analyze structure in various ways

Basic ideas
What is a graph?
An abstract representation of nodes connected by links.
Two ways of dealing with graphs:
☉ mathematical analysis (graph statistics, structural measures, etc.)
☉ visualization (network diagram, matrix, arc diagram, etc.)

Three different force-based layouts of my FB profile
OpenOrd, ForceAtlas, Fruchterman-Reingold

Non force-based layouts
Circle diagram, parallel bubble lines, arc diagram

Network statistics
betweenness centrality
degree
Relational elements of graphs can
be represented as tables (nodes
have properties) and analyzed
through statistics.
Network statistics bridge the gap
between individual units and the
structural forms they are
embedded in.
This is currently an extremely
prolific field of research.

Basic ideas

Basic ideas
What is a graph?
Vertices and edges!
Nodes and lines!
Two main types:
Directed (e.g. Twitter)
Undirected (e.g. Facebook)
Properties of nodes:
degree, centrality, etc.
Properties of edges:
weight, direction, etc.
Properties of the graph:
averages, diameter, communities, etc.

Basic ideas

Basic ideas
Wikipedia: Glossary of graph theory
Tools are easy, concepts are hard

Basic ideas
Interactive visual analytics
Bringing structure to the surface (gephi panel: "layout")
☉ different spatializations (force, geometry, etc.)
Projecting variables into the diagram (gephi panel: "ranking")
☉ Size (nodes, edges, labels, etc.)
☉ Color (nodes, edges, labels, etc.)
Deriving measures (gephi panel: "statistics")
☉ Properties of nodes, edges, structure => new variables
Analysis: e.g. correlation between spatial layout and variables?

Basic ideashttp://courses.polsys.net/gephi/

Basic ideas

Basic ideas
Twitter #ows dataset, co-hashtag analysis
Strong topic clustering

Twitter 1% sample, co-hashtag analysis
227,029 unique hashtags, 1627 displayed (freq >= 50)
Size: frequency
Color: modularity

Size: frequency
Color: user diversity
Twitter 1% sample, co-hashtag analysis
227,029 unique hashtags, 1627 displayed (freq >= 50)

Size: frequency
Color: degree
Twitter 1% sample, co-hashtag analysis
227,029 unique hashtags, 1627 displayed (freq >= 50)

Twitter 1% sample
Co-hashtag analysis
Degree vs.
wordFrequency

Degree vs. userDiversity
Twitter 1% sample
Co-hashtag analysis

FB group "Islam is dangerous"
Friendship network, color: betweenness centrality
2.339 members
Average degree of 39.69
81.7% have at least one friend in the group
55.4% five or more
37.2% have 20 or more
founder and admin has 609 friends

FB group "Islam is dangerous"
Friendship network, color: Interface language
en_us, de, en_uk, it dominate

Mapping European Extremism
Friendship relations of 18 extreme-right groups

FB page "Educate children about the evils of Islam"
Links have more comments, photos more likes.

FB page "Stop the Islamization
of the World"
Number of posts and reactions

FB page "Stop the
Islamization of the World"

Basic ideas
Interactive visual analytics
Bringing structure to the surface (gephi panel: "layout")
☉ different spatializations (force, geometry, etc.)
Projecting variables into the diagram (gephi panel: "ranking")
☉ Size (nodes, edges, labels, etc.)
☉ Color (nodes, edges, labels, etc.)
Deriving measures (gephi panel: "statistics")
☉ Properties of nodes, edges, structure => new variables
Analysis: e.g. correlation between spatial layout and variables?

Basic ideashttp://courses.polsys.net/gephi/

Nine measures of centrality (Freeman 1979)

Label PR α=0.85 PR α=0.7 PR α=0.55 PR α=0.4 In-Degree Out-Degree Degree
n34 0.0944 0.0743 0.0584 0.0460 4 1 5
n1 0.0867 0.0617 0.0450 0.0345 1 2 3
n17 0.0668 0.0521 0.0423 0.0355 2 1 3
n39 0.0663 0.0541 0.0453 0.0388 5 1 6
n22 0.0619 0.0506 0.0441 0.0393 5 1 6
n27 0.0591 0.0451 0.0371 0.0318 1 0 1
n38 0.0522 0.0561 0.0542 0.0486 6 0 6
n11 0.0492 0.0372 0.0306 0.0274 3 1 4

Basic ideas
US Airports

Thank You
rieder@uva.nl
https://www.digitalmethods.net
http://thepoliticsofsystems.net
"Far better an approximate answer to the right question,
which is often vague, than an exact answer to the wrong
question, which can always be made precise. Data
analysis must progress by approximate answers, at best,
since its knowledge of what the problem really is will at
best be approximate." (Tukey 1962)