Fast Inbound Top-K Query for Random Walk with Restart

Random walk with restart (RWR) is widely recognized as one of the most important node proximity measures for graphs, as it captures the holistic graph structure and is robust to noise in the graph. In this paper, we study a novel query based on the RWR measure, called the inbound top-k (Ink) query. Given a query node q and a number k, the Ink query aims at retrieving k nodes in the graph that have the largest weighted RWR scores to q. Ink queries can be highly useful for various applications such as traffic scheduling, disease treatment, and targeted advertising. Nevertheless, none of the existing RWR computation techniques can accurately and efficiently process the Ink query in large graphs. We propose two algorithms, namely SQUEEZE and RIPPLE, both of which can accurately answer the Ink query in a fast and incremental manner. To identify the top-k nodes, SQUEEZE iteratively performs matrix-vector multiplication and estimates the lower and upper bounds for all the nodes in the graph...

• Type: Journal
• Year: 2015
• Where: PKDD
• Authors: Chao Zhang, Shan Jiang, Yucheng Chen, Yidan Sun, Jiawei Han
• Keywords: Data Mining, PKDD 2015