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A kernel method for unsupervised structured network inference

2009

Conference Paper

ei


Network inference is the problem of inferring edges between a set of real-world objects, for instance, interactions between pairs of proteins in bioinformatics. Current kernel-based approaches to this problem share a set of common features: (i) they are supervised and hence require labeled training data; (ii) edges in the network are treated as mutually independent and hence topological properties are largely ignored; (iii) they lack a statistical interpretation. We argue that these common assumptions are often undesirable for network inference, and propose (i) an unsupervised kernel method (ii) that takes the global structure of the network into account and (iii) is statistically motivated. We show that our approach can explain commonly used heuristics in statistical terms. In experiments on social networks, different variants of our method demonstrate appealing predictive performance.

Author(s): Lippert, C. and Stegle, O. and Ghahramani, Z. and Borgwardt, KM.
Book Title: JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009
Journal: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009)
Pages: 368-375
Year: 2009
Month: April
Day: 0
Editors: Van Dyk, D. , M. Welling
Publisher: MIT Press

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

Event Name: Twelfth International Conference on Artificial Intelligence and Statistics
Event Place: Clearwater Beach, FL, USA

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5663,
  title = {A kernel method for unsupervised structured network inference},
  author = {Lippert, C. and Stegle, O. and Ghahramani, Z. and Borgwardt, KM.},
  journal = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009},
  pages = {368-375},
  editors = {Van Dyk, D. , M. Welling},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = apr,
  year = {2009},
  month_numeric = {4}
}