Interactive visualization and exploration
of network data with gephi
Bernhard Rieder
Universiteit van Amsterdam
Mediastudi...
Two kinds of mathematics
Can there be data analysis without math? No.
Does this imply epistemological commitments? Yes.
Bu...
Two kinds of mathematics
Statistics
Observed: objects and properties
Inferred: social forces
Data representation: the tabl...
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, ...
Basic ideas
Moreno 1934
Graph theory developed in
exchange with sociometry,
small-group research and
(later) social exchan...
Basic ideas
Forsythe and Katz, 1946, "adjacency matrix"
Harary, Graph Theory, 1969
Basic ideas
The network singularity
Why do network analysis and visualization? Which arguments are put
forward?
☉ New medi...
Basic ideas
Adamic and Glance, "Divided They Blog", 2005
Graph theory
Graph theory consists of or provides:
☉ A basic conceptual and formal model (point and line)
☉ Descriptive an...
Formalization
"As we have seen, the basic terms of digraph theory are point and line. Thus, if an
appropriate coordination...
Much of these data can be
analyzed as graphs.
Social media formalize
interaction at the interface.
Basic ideas
What Kind of Phenomena/Data?
Interactive networks (Watts 2004): link encodes tangible interaction
☉ social net...
Basic ideas
File formats
To be able to begin, we need data in a graph file format. There are a
number of different file fo...
Basic ideas
What is a graph?
An abstract representation of nodes connected by links.
Two ways of analyzing graphs:
☉ numer...
Basic ideas
Wikipedia: Glossary of graph theory
Tools are easy, concepts are hard
http://courses.polsys.net/gephi/
Vertices and edges!
Nodes and lines!
Two main types:
Directed (e.g. Twitter)
Undirected (e.g. Facebook)
Properties of node...
Basic ideas
Basic ideashttp://courses.polsys.net/gephi/
Basic ideas
Interactive visual analytics
Bringing structure to the surface (gephi panel: "layout")
☉ different spatializat...
Layout algorithms transform n-dimensional
adjacency matrices into two-dimensional diagrams
Every algorithm/technique reveals the structure
of the graph differently, shows different aspects
Basic ideas
Interactive visual analytics
Bringing structure to the surface (gephi panel: "layout")
☉ different spatializat...
Basic ideas
Nine measures of centrality (Freeman 1979)
Basic ideas
Interactive visual analytics
Bringing structure to the surface (gephi panel: "layout")
☉ different spatializat...
Basic ideas
Basic ideas
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....
FB group "Islam is dangerous"
Friendship network, color: betweenness centrality
2.339 members
Average degree of 39.69
81.7...
Twitter 1% sample, co-hashtag analysis
227,029 unique hashtags, 1627 displayed (freq >= 50)
Size: frequency
Color: modular...
Size: frequency
Color: user diversity
Twitter 1% sample, co-hashtag analysis
227,029 unique hashtags, 1627 displayed (freq...
Size: frequency
Color: degree
Twitter 1% sample, co-hashtag analysis
227,029 unique hashtags, 1627 displayed (freq >= 50)
Network statistics
betweenness centrality
degree
Relational elements of graphs can
be represented as tables (nodes
have pr...
Twitter 1% sample
Co-hashtag analysis
Degree vs.
wordFrequency
Degree vs. userDiversity
Twitter 1% sample
Co-hashtag analysis
Basic ideas
PlugIn: Spatial Ranking
Co-like analysis of my personal FB network:
Nodes: users / Links: "liking the same thing"
Example 3: our imagination
Basic ideas
PlugIn: Multimodal Projection
Basic ideas
Basic ideas
PlugIn: GeoLayout
Thank You
rieder@uva.nl
https://www.digitalmethods.net
http://thepoliticsofsystems.net
"Far better an approximate answer t...
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Interactive visualization and exploration of network data with Gephi

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

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  • Tukey, The Future of Data Analysis, 1962We don’t have rigorous methods for hypothesis testing in network analysis.
  • Allows for all kinds of folding, combinations, etc. – Math is not homogeneous, but sprawling!Different forms of reasoning, different modes of aggregation.These are already analytical frameworks, different ways of formalizing.Statistics: atomism, structure is implicit ("hidden forces", "social forces" cf. Durhkeim) => groups are abstractions, constituted by socioeconomic similaritySocial Network Analysis: atomism, structure is explicit ("dyadic forces") => groups are concrete, constituted by social exchange
  • Now we can calculate (in particular via matrix algebra).
  • Handbooks on graph theory are full of exhaustive discussions of basic graph types. Loads of vocabulary and analytical approaches.
  • Handbooks on graph theory are full of exhaustive discussions of basic graph types. Loads of vocabulary and analytical approaches.
  • Very large scale systems on the one side, but highly concentrated data repositories on the other.The promise of data analysis is, of course, to use that data to make sense of all the complexity.
  • Visualization is, again, one type of analysis.Which properties of the network are "made salient" by an algorithm?http://thepoliticsofsystems.net/2010/10/one-network-and-four-algorithms/Models behind: spring simulation, simulated annealing (http://wiki.cns.iu.edu/pages/viewpage.action?pageId=1704113)
  • Non force-based layouts can be extremely useful. Gephi can produce those as well
  • Network analysis has produced a large number of calculated metrics that take into account the structure of the network."All in all, this process resulted in the specification of nine centrality measures based on three conceptual foundations. Three are based on the degrees of points and are indexes of communication activity. Three are based on the betweenness of points and are indexes of potential for control of communication. And three are based on closeness and are indexes either of independence or efficiency." (Freeman 1979)What concepts are these metrics based on?
  • Network metrics are highly dependent on individual variables. Here: the same network with PageRank with four different values for the dampening parameter alpha. (red=highest PR value, yellow=second highest, turquoise=third highest)See Rieder 2012: http://computationalculture.net/article/what_is_in_pagerank
  • From DMI workshop on anti-Islamism and right-wing extremism.We can also look at interaction patters: activity structure, held together by leaders?
  • Extend word lists (what am I missing?), account for refraction. Rieder & Gerlitz 2013: http://journal.media-culture.org.au/index.php/mcjournal/article/viewArticle/620Rieder 2012: http://firstmonday.org/ojs/index.php/fm/article/view/4199/3359
  • Project variables into the graph User diversity = no of unique users of a hashtag divided by hashtag frequency
  • Larger roles of hashtags, not all are issue markers!
  • There is no need to analyze and visualize a graph as a network.Characterize hashtags in relation to a whole. (their role beyond a particular topic sample), better understand our "fishing pole" (the sample technique) and the weight it carries.Tbt: throwback thursday
  • This is a technical process, but to be a method, there needs to be adequation between a conceptual element and a technical one.These steps translate a large number of commitments to particular ideas.A postdemographic (Rogers) approach.
  • Interactive visualization and exploration of network data with Gephi

    1. 1. Interactive visualization and exploration of network data with gephi Bernhard Rieder Universiteit van Amsterdam Mediastudies Department and some conceptual context
    2. 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. 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. 4. Graph theory Leonhard Euler, "Seven Bridges of Königsberg", 1735 Introducing the "point and line" model
    5. 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. 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. 7. Basic ideas
    8. 8. Forsythe and Katz, 1946, "adjacency matrix"
    9. 9. Harary, Graph Theory, 1969
    10. 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. 11. Basic ideas Adamic and Glance, "Divided They Blog", 2005
    12. 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. 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. 14. Much of these data can be analyzed as graphs. Social media formalize interaction at the interface.
    15. 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. 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. 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. 18. Basic ideas Wikipedia: Glossary of graph theory Tools are easy, concepts are hard http://courses.polsys.net/gephi/
    19. 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. 20. Basic ideas
    21. 21. Basic ideashttp://courses.polsys.net/gephi/
    22. 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. 23. Layout algorithms transform n-dimensional adjacency matrices into two-dimensional diagrams
    24. 24. Every algorithm/technique reveals the structure of the graph differently, shows different aspects
    25. 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. 26. Basic ideas
    27. 27. Nine measures of centrality (Freeman 1979)
    28. 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. 29. Basic ideas
    30. 30. Basic ideas
    31. 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. 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. 33. Twitter 1% sample, co-hashtag analysis 227,029 unique hashtags, 1627 displayed (freq >= 50) Size: frequency Color: modularity
    34. 34. Size: frequency Color: user diversity Twitter 1% sample, co-hashtag analysis 227,029 unique hashtags, 1627 displayed (freq >= 50)
    35. 35. Size: frequency Color: degree Twitter 1% sample, co-hashtag analysis 227,029 unique hashtags, 1627 displayed (freq >= 50)
    36. 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. 37. Twitter 1% sample Co-hashtag analysis Degree vs. wordFrequency
    38. 38. Degree vs. userDiversity Twitter 1% sample Co-hashtag analysis
    39. 39. Basic ideas PlugIn: Spatial Ranking
    40. 40. Co-like analysis of my personal FB network: Nodes: users / Links: "liking the same thing" Example 3: our imagination
    41. 41. Basic ideas PlugIn: Multimodal Projection
    42. 42. Basic ideas
    43. 43. Basic ideas PlugIn: GeoLayout
    44. 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)

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