Abstract
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.
Highlights
IntroductionThe analysis of complex networks, e.g., by investigating structural properties and identifying interesting patterns, is an important task to make sense of such networks, in order to enable an understanding of their phenomena and structures, e.g., (Newman 2003; Kumar et al 2006; Almendral et al 2007; Mitzlaff et al 2011; Silva et al 2012; Mitzlaff et al 2013; Atzmueller 2014; Pool et al 2014; Galbrun et al 2014; Mitzlaff et al 2014; Kibanov et al 2014; Soldano et al 2015; Atzmueller et al 2016; Bendimerad et al 2016; Kaytoue et al 2017; Atzmueller 2017; 2019)
We demonstrate the efficacy of our presented novel method MinerLSD by performing experiments on several standard datasets, in relation to two baselines for local pattern mining
The algorithm MinerLSD that we propose in “The MinerLSD Algorithm” section, closely follows the MinerLC algorithm and adds requirements regarding the local modularity of the pattern core subgraphs
Summary
The analysis of complex networks, e.g., by investigating structural properties and identifying interesting patterns, is an important task to make sense of such networks, in order to enable an understanding of their phenomena and structures, e.g., (Newman 2003; Kumar et al 2006; Almendral et al 2007; Mitzlaff et al 2011; Silva et al 2012; Mitzlaff et al 2013; Atzmueller 2014; Pool et al 2014; Galbrun et al 2014; Mitzlaff et al 2014; Kibanov et al 2014; Soldano et al 2015; Atzmueller et al 2016; Bendimerad et al 2016; Kaytoue et al 2017; Atzmueller 2017; 2019). Efficient top-down enumeration algorithms exist as far as the constraints are anti-monotonic: whenever the constraint fails to be satisfied by some pattern, it fails for all more specific patterns This is obviously the case for the minimum support constraint that requires the size of ext(q) to be above some minimal support threshold s
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