@inproceedings{4950,
  title = {Graph Mining with Variational Dirichlet Process Mixture Models},
  journal = {Proceedings of the 8th SIAM International Conference on Data Mining},
  booktitle = {SDM 2008},
  abstract = {Graph data such as chemical compounds and XML documents are getting more
  common in many application domains.
  A main difficulty of graph data processing
  lies in the intrinsic high dimensionality of graphs, namely,
  when a graph is represented as a binary feature vector
  of indicators of all possible subgraph patterns,
  the dimensionality gets too large for usual statistical methods.
  We propose a nonparametric Bayesian method for clustering graphs and
  selecting salient patterns at the same time.
  Variational inference is adopted here, because sampling
  is not applicable due to extremely high dimensionality.
  The feature set minimizing the free energy is efficiently
  collected with the DFS code tree, where the generation of useless subgraphs
  is suppressed by a tree pruning condition.
  In experiments, our method is compared with a simpler approach based on
  frequent subgraph mining, and graph kernels.},
  pages = {432-442},
  editors = {Zaki, M. J.},
  publisher = {Society for Industrial and Applied Mathematics},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Philadelphia, PA, USA},
  month = apr,
  year = {2008},
  slug = {4950},
  author = {Tsuda, K. and Kurihara, K.},
  month_numeric = {4}
}