Efficient Mining of Subsample-Stable Graph Patterns

Aleksey Buzmakov 1 Sergei Kuznetsov 2 Amedeo Napoli 3
3 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of anti-monotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns. The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.
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Communication dans un congrès
ICDM 2017 - 17th IEEE International Conference on Data Mining, Nov 2017, New Orleans, United States. pp.1-6, 〈http://icdm2017.bigke.org/〉
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Soumis le : mercredi 20 décembre 2017 - 11:00:13
Dernière modification le : jeudi 11 janvier 2018 - 06:25:24

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Aleksey Buzmakov, Sergei Kuznetsov, Amedeo Napoli. Efficient Mining of Subsample-Stable Graph Patterns. ICDM 2017 - 17th IEEE International Conference on Data Mining, Nov 2017, New Orleans, United States. pp.1-6, 〈http://icdm2017.bigke.org/〉. 〈hal-01668663〉

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