@inproceedings{5204,
  title = {Partial Least Squares Regression for Graph Mining},
  journal = {Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008)},
  booktitle = {KDD2008},
  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.},
  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},
  slug = {5204},
  author = {Saigo, H. and Kr{\"a}mer, N. and Tsuda, K.},
  month_numeric = {8}
}