About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group Marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith
Central tenet – Social structure emerges from – the aggregate of relationships (ties) – among members of a population Phenomena of interest – Emergence of cliques and clusters – from patterns of relationships – Centrality (core), periphery (isolates), – betweenness Methods – Surveys, interviews, observations, log file analysis, computational analysis of matrices (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001) Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7- 16 Social Network Theory http://en.wikipedia.org/wiki/Social_network http://en.wikipedia.org/wiki/Social_network
Social Networks History: from the dawn of time! Theory and method: 1934 -> Jacob L. Moreno http://en.wiki pedia.org/wiki /Jacob_L._Mor eno http://en.wiki pedia.org/wiki /Jacob_L._Mor eno
SNA 101 Node –“actor” on which relationships act; 1-mode versus 2-mode networks Edge –Relationship connecting nodes; can be directional Cohesive Sub-Group –Well-connected group; clique; cluster Key Metrics –Centrality (group or individual measure) Number of direct connections that individuals have with others in the group (usually look at incoming connections only) Measure at the individual node or group level –Cohesion (group measure) Ease with which a network can connect Aggregate measure of shortest path between each node pair at network level reflects average distance –Density (group measure) Robustness of the network Number of connections that exist in the group out of 100% possible –Betweenness (individual measure) # shortest paths between each node pair that a node is on Measure at the individual node level Node roles –Peripheral – below average centrality –Central connector – above average centrality –Broker – above average betweenness E D F A C B H G I C D E ABDE
Two “answer people” with an emerging 3 rd. Mapping Newsgroup Social Ties Microsoft.public.windowsxp.server.general 8
Distinguishing attributes of online social roles Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties Reply Magnet – Ties from local isolates often inward only – Sparse, few triangles – Few intense ties 9
Distinguishing attributes: Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties Discussion person – Ties from local isolates often inward only – Dense, many triangles – Numerous intense ties 10
A minimal network can illustrate the ways different locations have different values for centrality and degree Diane has high degree Heather has high betweeness NodeXL Network Overview Discovery and Exploration add-in for Excel 2007
NodeXL: Display nodes with subgraph images sorted by network attributes using Excel Data|Sort
Network Clusters visualization showing three Flickr tag clusters, each representing a different context for “mouse”.
Isolate clusters showing three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse character.
NodeXL Network of Flickr users who comment on Marc_Smith’s photos (network depth 1.5; edge weight ≥ 4).
Import data from Twitter user and term networks
NodeXL Tutorial http://casci.umd.edu/ Book forthcoming: Analyzing social media networks with NodeXL: Insights from a connected world Book forthcoming: Analyzing social media networks with NodeXL: Insights from a connected world
Social media network archives On-going collection Additional sources: enterprise/consumer More metrics Performance Cross-platform/Web Clustering Time series analysis