Discriminative frequent subgraph mining with optimality guarantees

DOI10.1002/sam.10084OpenAlexW4230608978WikidataQ61465943 ScholiaQ61465943MaRDI QIDQ4969732

Karsten M. Borgwardt, Philip S. Yu, Hong Cheng, Jiawei Han, Xifeng Yan, Marisa Thoma, Le Song, Arthur Gretton, Alexander J. Smola, Hans-Peter Kriegel

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10084

zbMATH Keywords

submodularityfeature selectiongraph miningfrequent subgraphsnested selection

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (2)

Effective graph classification based on topological and label attributes ⋮ Distance metric learning for graph structured data

Uses Software

• LIBSVM
• AFGen
• gSpan
• MoSS
• gBoost

Cites Work

• Unnamed Item
• Finding essential attributes from binary data
• gBoost: a mathematical programming approach to graph classification and regression
• An analysis of approximations for maximizing submodular set functions—I
• Machine Learning: ECML 2004

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