Learning graph representation via frequent subgraphs

Nguyen, Dang Pham Hai, Luo, Wei, Nguyen, Tu Dinh, Venkatesh, Svetha and Phung, Quoc-Dinh 2018, Learning graph representation via frequent subgraphs, in SDM 2018 : Proceedings of the SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Philadelphia, Pa., pp. 306-314, doi: 10.1137/1.9781611975321.

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Title Learning graph representation via frequent subgraphs
Author(s) Nguyen, Dang Pham Hai
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Nguyen, Tu Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Conference name Data Mining. SIAM International Conference (2018 : San Diego, Calif.)
Conference location San Diego, Calif.
Conference dates 2018/05/03 - 2018/05/05
Title of proceedings SDM 2018 : Proceedings of the SIAM International Conference on Data Mining
Editor(s) Ester, M.
Pedreschi, D.
Publication date 2018
Start page 306
End page 314
Total pages 9
Publisher Society for Industrial and Applied Mathematics
Place of publication Philadelphia, Pa.
Summary © 2018 by SIAM. We propose a novel approach to learn distributed representation for graph data. Our idea is to combine a recently introduced neural document embedding model with a traditional pattern mining technique, by treating a graph as a document and frequent subgraphs as atomic units for the embedding process. Compared to the latest graph embedding methods, our proposed method offers three key advantages: fully unsupervised learning, entire-graph embedding, and edge label leveraging. We demonstrate our method on several datasets in comparison with a comprehensive list of up-to-date stateof-the-art baselines where we show its advantages for both classification and clustering tasks.
ISBN 9781611975321
Language eng
DOI 10.1137/1.9781611975321
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2018, SIAM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109588

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