MinerLSD: efficient mining of local patterns on attributed networks
| Autor: | Henry Soldano, Dominique Bouthinon, Guillaume Santini, Martin Atzmueller |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Multidisciplinary
Exploit Community detection Computer Networks and Communications Computer science Closed pattern mining lcsh:T57-57.97 Complex networks Attributed networks computer.software_genre 01 natural sciences 010305 fluids & plasmas Local pattern Computational Mathematics Network analysis and mining 0103 physical sciences lcsh:Applied mathematics. Quantitative methods Graph (abstract data type) Data mining Pattern space Graph mining 010306 general physics computer Network analysis |
| Zdroj: | Applied Network Science, Vol 4, Iss 1, Pp 1-33 (2019) |
| ISSN: | 2364-8228 |
| DOI: | 10.1007/s41109-019-0155-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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