Fast computation of SimRank for static and dynamic information networks

Information networks are ubiquitous in many applications and analysis on such networks has attracted significant attention in the academic communities. One of the most important aspects of information network analysis is to measure similarity between nodes in a network. SimRank is a simple and influential measure of this kind, based on a solid theoretical “random surfer” model. Existing work computes SimRank similarity scores in an iterative mode. We argue that the iterative method can be infeasible and inefficient when, as in many real-world scenarios, the networks change dynamically and frequently. We envision non-iterative method to bridge the gap. It allows users not only to update the similarity scores incrementally, but also to derive similarity scores for an arbitrary subset of nodes. To enable the non-iterative computation, we propose to rewrite the SimRank equation into a non-iterative form by using the Kronecker product and vectorization operators. Based on this, we d...

• Type: Conference
• Year: 2010
• Where: EDBT
• Authors: Cuiping Li, Jiawei Han, Guoming He, Xin Jin, Yizhou Sun, Yintao Yu, Tianyi Wu
• Keywords: Computes Simrank Similarity, Database, EDBT 2010, Information Network, Similarity Scores