Partial Least Squares Regression for Graph Mining

Saigo, H; Krämer, N; Tsuda, K

doi:10.1145/1401890.1401961

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Partial Least Squares Regression for Graph Mining

Saigo, H., Krämer, N., & Tsuda, K. (2008). Partial Least Squares Regression for Graph Mining. In Y. Li, B. Liu, & S. Sarawagi (Eds.), KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 578-586). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C7BC-7 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-3D39-A

Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1401890.1401961 (Publisher version) Open Access status unknown

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Creators:
Saigo, H^{1, 2}, Author
Krämer, N, Author
Tsuda, K^{1, 2}, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2biological cy, ou_persistent22

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Abstract: Attributed graphs are increasingly more common in many application
domains such as chemistry, biology and text processing.
A central issue in graph mining is how to collect informative subgraph
patterns for a given learning task.
We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm.
The mining algorithm is iteratively called with different weight
vectors, creating one latent component per one mining call.
Our method, graph PLS, is efficient and easy to implement, because the
weight vector is updated with elementary matrix calculations.
In experiments, our graph PLS algorithm showed
competitive prediction accuracies in many chemical datasets and its
efficiency was significantly superior to graph boosting (gboost) and the
naive method based on frequent graph mining.

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Dates: Date issued: 2008-08

Publication Status: Issued

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Identifiers: DOI: 10.1145/1401890.1401961
BibTex Citekey: 5204

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Title: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Place of Event: Las Vegas, NV, USA

Start-/End Date: 2008-08-24 - 2008-08-27

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Title: KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Source Genre: Proceedings

Creator(s):
Li, Y, Editor
Liu, B, Editor
Sarawagi, S, Editor

Affiliations:
-

Publ. Info: New York, NY, USA : ACM Press

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 578 - 586 Identifier: ISBN: 978-1-60558-193-4