MinerLSD: Efficient Local Pattern Mining on Attributed Graphs

Abstract

Local pattern mining on attributed graphs is an important and interesting research area combining ideas from network analysis and graph mining. In this paper, we present MinerLSD, a method for efficient local pattern mining on attributed graphs. In order to prevent the typical pattern explosion in pattern mining, we employ closed patterns for focusing pattern exploration. In addition, we exploit efficient techniques for pruning the pattern space: We adapt a local variant of the Modularity metric with optimistic estimates, and include graph abstractions. Our experiments on several standard datasets demonstrate the efficacy of our proposed novel method MinerLSD as an efficient method for local pattern mining on attributed graphs.