Colibri: fast mining of large static and dynamic graphs

Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track the low-rank structure as the graph evolves over time, efficiently and within limited storage. Real graphs typically have thousands or millions of nodes, but are usually very sparse. However, standard decompositions such as SVD do not preserve sparsity. This has led to the development of methods such as CUR and CMD, which seek a nonorthogonal basis by sampling the columns and/or rows of the sparse matrix. However, these approaches will typically produce overcomplete bases, which wastes both space and time. In this paper we propose the family of Colibri methods to deal with these challenges. Our version for static graphs, Colibri-S, iteratively finds a nonredundant basis and we prove that it has no loss of accuracy compared to the best competitors (CUR and CMD), while achieving significant savings in space and...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, Philip S. Yu, Christos Faloutsos
• Keywords: Data Mining, Graph Mining Permission, KDD 2008, Keywords Low-rank Approximation, Low-rank Approximations