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Issue No. 04 - April (2015 vol. 27)
ISSN: 1041-4347
pp: 1112-1131
Ryan A. Rossi , Department of Computer Science, Purdue University, West Lafayette, IN
Nesreen K. Ahmed , Department of Computer Science, Purdue University, West Lafayette, IN
ABSTRACT
Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit these early notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of three general classes of techniques for discovering roles that includes(i) graph-based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose a flexible framework for discovering roles using the notion of similarity on a feature-based representation. The framework consists of two fundamental components: (a) role featureconstruction and (b) role assignment using the learned feature representation. We discuss the different possibilities for discoveringfeature-based roles and the tradeoffs of the many techniques for computing them. Finally, we discuss potential applications and future directions and challenges.
INDEX TERMS
Communities, Taxonomy, Computational modeling, Social network services, Stochastic processes, Correlation, Bridges,unsupervised learning, Roles, role discovery, role learning, feature-based roles, structural similarity
CITATION
Ryan A. Rossi, Nesreen K. Ahmed, "Role Discovery in Networks", IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. , pp. 1112-1131, April 2015, doi:10.1109/TKDE.2014.2349913
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