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Partial Least Squares Regression for Graph Mining

2008

Conference Paper

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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.

Author(s): Saigo, H. and Krämer, N. and Tsuda, K.
Book Title: KDD2008
Journal: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008)
Pages: 578-586
Year: 2008
Month: August
Day: 0
Editors: Li, Y. , B. Liu, S. Sarawagi
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1401890.1401961
Event Name: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Event Place: Las Vegas, NV, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5204,
  title = {Partial Least Squares Regression for Graph Mining},
  author = {Saigo, H. and Kr{\"a}mer, N. and Tsuda, K.},
  journal = {Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008)},
  booktitle = {KDD2008},
  pages = {578-586},
  editors = {Li, Y. , B. Liu, S. Sarawagi},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = aug,
  year = {2008},
  month_numeric = {8}
}