Random walk with restart on large graphs using block elimination

摘要: Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes a long time, various preprocessing methods, most of which are related to inverting adjacency matrices, have been proposed to speed up the calculation. However, these methods do not scale to large graphs because they usually produce large dense matrices that do not fit into memory. In addition, the existing methods are inappropriate when graphs dynamically change because the expensive preprocessing task needs to be computed repeatedly. In this article, we propose Bear, a fast, scalable, and accurate method for computing RWR on large graphs. Bear has two …