S OCIAL N ETWORK A NALYSIS F OR D UMMIES Y ANNE B ROUX DH S UMMER S CHOOL L EUVEN, S EPTEMBER
T ERMINOLOGY
Useful sources A.-L. B ARABÁSI, Linked: The Science of Networks (Cambridge, 2002) S. B ORGATTI et al., Analyzing Social Networks (L.A., 2013) Y. B ROUX & S. V ANBESELAERE, Six Degrees of Spaghetti Monsters (spaghetti-os.blogspot.com)
Basics Node (vertex) Edge (tie) – Undirected – Directed – Weighted (valued) Degree: how many edges to a node – Undirected: count edges – Directed: indegree vs outdegree A B C D E F
D ATA MANAGEMENT
Adjacency matrix Symmetric, binary e.g. who knows who Symmetric, weighted e.g. distance between places
Adjacency matrix Asymmetric, binary e.g. choose 3 friends to sit with Asymmetric, weighted e.g. number of s sent to colleagues
One-mode vs two-mode 1-mode: direct ties between actors (= adjacency matrix) 2-mode: ties between different entities (= affiliation matrix)
Adjacency vs attribute matrix Adjacency matrix: only records ties between nodes Attribute matrix: each column is different attribute of the nodes (gender, role, ethnicity, status, …) = ‘nodelist’ (vs ‘edgelist’)
Attribute matrix (nodelist)
S OFTWARE
UCINET + Netdraw + Almost anything you need for SNA is in here, very advanced statistics + All statistics can be loaded into visualization package + Free (re-download after each trial version) - Can’t handle large datasets (3000+ nodes) - Only for Windows (Mac: run with Wine but not all features work) - Crude visualizations
R + Very, very comprehensive + Not only SNA, everything statistical + Free + Open source - Steep learning curve (programming language) - Difficulty with Big Data - Very crude visualizations
NodeXL + Fancy visualizations + Easy interface (integrated in Excel!) + Free - Doesn’t work for Mac
Gephi + Free + More sophisticated visualizations + Easy import from Excel - Less (accurate) possibilities for analysis - Some bugs when reopening saved files - No user guides. You’re on your own not anymore! spaghetti-os.blogspot.comspaghetti-os.blogspot.com
SNA AND T RISMEGISTOS
Late Republican affairs
Co-publication
Names – Hermopolis AD Greek Egyptian Latin unknown 322 names (based on 621 individuals) 471 edges
Place names in Egypt
Three- mode network year text person
G ETTING STARTED WITH G EPHI
Nodelist
Edgelist
Communities
‘Modularity’: automated community detection Density within clusters vs between them
Centrality measures A = betweenness B = closeness C = eigenvector D = degree
Centrality measures
PageRank