Efficient mining of subsample-stable graph patterns
A Buzmakov, SO Kuznetsov… - 2017 IEEE International …, 2017 - ieeexplore.ieee.org
2017 IEEE International Conference on Data Mining (ICDM), 2017•ieeexplore.ieee.org
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.
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.
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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