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

Uncovering Cohesive GroupsFreeman, LintonNetwork MethodsFRI.AM1Since 1998, when social network analysis became part of physics, there has been a revolution in the field. More articles are published. Wider applications areproposed. And new approaches to traditional problems are developed. Among traditional problems in social network analysis is concern with cohesivegroups. Since the 1930s we have attempted to specify their structural properties and we have developed procedures to uncover them in data sets (cf. Moreno,1934; Davis, Gardner and Gardner, 1941; Forsyth and Katz, 1946; Luce and Perry, 1949; Beum and Brundage, 1950; Homans, 1950). Since the revolution, thatthrust has continued and expanded. Physicists have produced new definitions of cohesive groups and they have proposed new algorithms for uncoveringthem. In physics, however, cohesive groups are called “communities.” The physicists have also enlisted the help of computer scientists in this endeavor. But,in computer science, cohesive groups or communities are called “clusters.” Here I will review some of the most used of these new approaches. In particular, Iwill focus on cohesive group‐community‐cluster finding algorithms (eg. spinglass, edge betweenness, leading Eigenvector, label propagation, walktrap, markovclustering, k‐clustering). By applying them to classic social network data sets, I will explore their potential for uncovering structural groups.Understand And Guide Scientific Research Team Assembly Through Social Network Analysis: Mapping, Recommender System, And OptimizationHuang, Meikuan ; Huang, Yun; Devlin, Hugh ; Fazel, Maryam ; Liu, Jordan ; Contractor, NoshirApplying Social Network Analysis to Clinical and Translational Science in four CTSA InstitutionsERGM/P*, Scientific Networks, Team Formation, Recommender, ScientometricsSAT.AM2This paper reports on a series of two studies to understand and optimize scientific team assembly in Clinical and Translational Science using scientometric andarchival data. First, we report on an p*/ERGM study using co‐authorship and citation networks to understand the precedents and outcomes of teamscollaborating on proposals. Prior collaboration is a key team assembly factor which has been found to have mixed effects on group performance. Based ontheories of transactive memory, homophily, and prior collaboration, this study examines how prior co‐authorship, citation, and citation similarity influenceteam formation and success in scientific research groups. We collected scientometric and archival data of 101 research proposal teams with a total of 185scientists who competed in 2 rounds of grant competitions in 2009 hosted by a Clinical and Translational Sciences Institute located in a Midwestern university.We tested ERGM models in PNet and found that there is a predominant network structure in the successful teams that members are likely to collaborate withthose they have a coauthorship relationship with. Second, guided by MTML framework, we report on the implementation and evaluation of a recommendationsystem (powered by C‐IKNOW) using such metrics as co‐authorship, affiliations, citation history etc, to help researchers more efficiently find collaborators.