Sunbelt XXXI International Network for Social Network ... - INSNA

Extracting Leadership And Influence Metrics From Social Networks Derived From Meeting TranscriptsBroniatowski, David A.Poster SessionText Analysis, Bayesian Methods, Social Network, LeadershipSAT.PM3The role of leadership in group decision‐making is not well‐understood. Computational social science methods and topic models in particular, may be appliedto analyze transcripts of different group decisions. This paper analyzes leadership behavior by the committee chairs in the U.S. Food and Drug Administration.The output of the analysis is a set of directed social networks that reveal different engagement strategies utilized across meetings.Extracting Subpopulations From Large NetworksZhang, Bin; Krackhardt, David; Krishnan, Ramaya; Doreian, PatrickNetwork MethodsMethods, Network Analysis, Clustering, sub graphFRI.AM1Until recently, collecting network data of a substantial size was a challenge, limiting much of our research to analyzing relatively small networks. Now,however, with the help of new information technologies, we find ourselves basking in very large data sets. Their large size has outstripped our ability toperform even rudimentary analysis on the networks as a whole. One solution to this problem is to extract a connected subgraph from this large network andanalyze its properties as a stand‐alone subpopulation. In this paper, we propose a method for extracting subpopulations that maintains two desirableproperties: 1) that it is efficient (so that it scales well), and 2) that it is effective at extracting a subgraph that is relatively self‐contained (i.e., that it has moreties within it than it does to nodes outside the subgraph). We develop a method for such extractions, called "Transitive Closure and Pruning" (T‐CLAP), andcompare it to two other popularly used community detection algorithms in the literature ‐‐ Newman's community detection algorithm and Clauset'scommunity detection algorithm. We find that T‐CLAP and Newman's algorithm both are effective, but that Newman's algorithm is orders of magnitudes slowerthan T‐CLAP. We find that T‐CLAP and Clauset's algorithm are both very efficient and scale well, but that T‐CLAP is superior to Clauset's algorithm in terms ofreturning effective subpopulations that would be useful to study.