A Digital Methods Summer School 2013 Gephi tutorial by Bernhard Rieder.

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

2. 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

3. 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)

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

5. 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)

6. 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")

7. Basic ideas

8. Forsythe and Katz, 1946, "adjacency matrix"

9. Harary, Graph Theory, 1969

10. Basic ideas
The network singularity
Why do network analysis and visualization? Which arguments are put
forward?
☉ New media: technical and conceptual structures modeled as networks
☉ The network imaginary: networks as analytical device and trending topic
☉ Calculative capacities: powerful techniques and tools
☉ Visualization: the network diagram, "visual analytics"
☉ Logistics: data, software, and hardware are available and cheap
☉ Methodology I: dissatisfaction with statistics => SNA
☉ Methodology II: a "new science of networks" (Watts 2005) emerged
☉ Society: diversification, problems with demographics / statistics / theory

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

12. Graph theory
Graph theory consists of or provides:
☉ A basic conceptual and formal model (point and line)
☉ Descriptive and analytical language to talk about specific graphs
☉ Extensive calculability of structure
☉ Various “native” (and non-native) forms of visualization

13. 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?

14. Much of these data can be
analyzed as graphs.
Social media formalize
interaction at the interface.

15. 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 formalized as point and line
=> do all kinds of analysis (SNA, transportation, text mining, etc.)
=> analyze structural properties in various ways

16. Basic ideas
File formats
To be able to begin, we need data in a graph file format. There are a
number of different file formats used to specify graphs.
Different formats have different capacities (e.g. .gexf allows to specify
time intervals).
The guess (.gdf) format:
http://courses.polsys.net/gephi/

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

18. Basic ideas
Wikipedia: Glossary of graph theory
Tools are easy, concepts are hard
http://courses.polsys.net/gephi/

19. 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
What is a graph?
A
B
C
D
a-b
b-d
b-c c-d
Nodes, Degree:
A: 1, B: 3, C: 2, D: 2
Nodes, Weighted Degree:
A: 1, B: 3, C: 3, D: 3
Edges, Weight:
a-b: 1, b-c: 1, b-d: 1, c-d: 2
Graph, diameter: 2
Graph, density: 0.667 (4 edges out of 6)
Graph, average shortest path: 1.334
Numbers are great for comparison!

20. Basic ideas

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

22. 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?

23. Layout algorithms transform n-dimensional
adjacency matrices into two-dimensional diagrams

24. Every algorithm/technique reveals the structure
of the graph differently, shows different aspects

25. 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.)
Analysis: e.g. “correlation” between spatial layout and variables?

26. Basic ideas

27. Nine measures of centrality (Freeman 1979)

28. 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?

29. Basic ideas

30. Basic ideas

31. 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

32. 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

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

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

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

36. 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.

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

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

39. Basic ideas
PlugIn: Spatial Ranking

40. Co-like analysis of my personal FB network:
Nodes: users / Links: "liking the same thing"
Example 3: our imagination

41. Basic ideas
PlugIn: Multimodal Projection

42. Basic ideas

43. Basic ideas
PlugIn: GeoLayout

44. 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)