子样本稳定图模式的高效挖掘

2017 IEEE International Conference on Data Mining (ICDM) Pub Date : 2017-11-18 DOI:10.1109/ICDM.2017.88

A. Buzmakov, S. Kuznetsov, A. Napoli

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引用次数: 7

摘要

提出了一种可扩展的挖掘在次抽样下稳定的图模式的方法。根据目前已知的定义，现有的子样本稳定性和鲁棒性度量不是反单调的。我们研究了图模式反单调性的一个更广泛的概念，使得子样本稳定性的度量成为反单调的。然后，我们提出了用于挖掘最子样本稳定的图模式的gSOFIA。在大量图数据集上的实验表明，gSOFIA对于发现子样本稳定的图模式是非常有效的。

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Efficient Mining of Subsample-Stable Graph Patterns

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 antimonotonicity 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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2017 IEEE International Conference on Data Mining (ICDM)

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