Axiomatic Ranking of Network Role Similarity
  A key task in social network and other complex network analysis is role analysis: describing and categorizing nodes according to how they interact with other nodes. Two nodes have the same role if they interact with equivalent sets of neighbors. The most fundamental role equivalence is automorphic equivalence. Unfortunately, the fastest algorithms known for graph automorphism are nonpolynomial. Moreover, since exact equivalence may be rare, a more meaningful task is to measure the role similarity between any two nodes. This task is closely related to the structural or link-based similarity problem that SimRank attempts to solve. However, SimRank and most of its offshoots are not sufficient because they do not fully recognize automorphically or structurally equivalent nodes. In this paper we tackle two problems. First, what are the necessary properties for a role similarity measure or metric? Second, how can we derive a role similarity measure satisfying these properties? For the first problem, we justify several axiomatic properties necessary for a role similarity measure or metric: range, maximal similarity, automorphic equivalence, transitive similarity, and the triangle inequality. For the second problem, we present RoleSim, a new similarity metric with a simple iterative computational method. We rigorously prove that RoleSim satisfies all the axiomatic properties. We also introduce an iceberg RoleSim algorithm which can guarantee to discover all pairs with RoleSim score no less than a user-defined threshold $\theta$ without computing the RoleSim for every pair. We demonstrate the superior interpretative power of RoleSim on both both synthetic and real datasets. 
Ruoming Jin, Victor E. Lee, Hui Hong
cs.SI physics.soc-ph