A scalable pattern mining approach to web graph compression with communities

A link server is a system designed to support efficient implementations of graph computations on the web graph. In this work, we present a compression scheme for the web graph specifically designed to accommodate community queries and other random access algorithms on link servers. We use a frequent pattern mining approach to extract meaningful connectivity formations. Our Virtual Node Miner achieves graph compression without sacrificing random access by generating virtual nodes from frequent itemsets in vertex adjacency lists. The mining phase guarantees scalability by bounding the pattern mining complexity to O(E log E). We facilitate global mining, relaxing the requirement for the graph to be sorted by URL, enabling discovery for both inter-domain as well as intra-domain patterns. As a consequence, the approach allows incremental graph updates. Further, it not only facilitates but can also expedite graph computations such as PageRank and local random walks by implementing them directly on the compressed graph. We demonstrate the effectiveness of the proposed approach on several publicly available large web graph data sets. Experimental results indicate that the proposed algorithm achieves a 10- to 15-fold compression on most real word web graph data sets