MinerLSD: efficient mining of local patterns on attributed networks

Applied Network Science

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[1] 0000 0001 0943 3265, grid.12295.3d, Tilburg University, Department of Cognitive Science and Artificial Intelligence, Warandelaan 2, 5037 AB, Tilburg, The Netherlands;0000 0004 1788 6194, grid.469994.f, Université Sorbonne Paris Cité, Paris, France;0000 0004 0452 7037, grid.462937.d, LIPN, Université Paris-13, SPC UMR-CNRS 7030, 93430, Villetaneuse, France;0000 0004 0452 7037, grid.462937.d, LIPN, Université Paris-13, SPC UMR-CNRS 7030, 93430, Villetaneuse, France;0000 0001 2174 9334, grid.410350.3, ISYEB UMR 7205, Museum National d’Histoire Naturelle, Paris, France;

关键词: Complex networks;    Attributed networks;    Closed pattern mining;    Network analysis and mining;    Graph mining;    Community detection;   

DOI  :  10.1007/s41109-019-0155-y

来源: publisher

【 摘 要 】

Local pattern mining on attributed networks is an important and interesting research area combining ideas from network analysis and data mining. In particular, local patterns on attributed networks allow both the characterization in terms of their structural (topological) as well as compositional features. In this paper, we present MinerLSD, a method for efficient local pattern mining on attributed networks. 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 standard Modularity metric used in community detection that is extended using optimistic estimates, and furthermore 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 networks.

【 授权许可】

CC BY