Fast incremental proximity search in large graphs

In this paper we investigate two aspects of ranking problems on large graphs. First, we augment the deterministic pruning algorithm in Sarkar and Moore (2007) with sampling techniques to compute approximately correct rankings with high probability under random walk based proximity measures at query time. Second, we prove some surprising locality properties of these proximity measures by examining the short term behavior of random walks. The proposed algorithm can answer queries on the fly without caching any information about the entire graph. We present empirical results on a 600, 000 node author-word-citation graph from the Citeseer domain on a single CPU machine where the average query processing time is around 4 seconds. We present quantifiable link prediction tasks. On most of them our techniques outperform Personalized Pagerank, a well-known diffusion based proximity measure.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Purnamrita Sarkar, Andrew W. Moore, Amit Prakash
• Keywords: Diffusion Based Proximity, ICML 2008, Machine Learning, Proximity Measures, Walk Based Proximity