Interactive visualization and explorationof network data with gephi

Bernhard RiederUniversiteit van AmsterdamMediastudies 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 ideasAdamic 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 comments

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

degr

ee

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)

Basic ideasUS 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)