Variational Bayes learning over multiple graphs

Motoki Shiga, Hiroshi Mamitsuka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Learning (or mining) patterns in graphs has become an important issue in a lot of applications, including web, text and biology. Our issue is graph clustering, i.e. clustering nodes (examples) in a given network. We deal with a situation that we have multiple graphs, sharing nodes but having different edges, where each graph can have only part of the entire true clusters which we call localized clusters, being found in only part of all given graphs. For this issue, we present a probabilistic generative model and its robust learning scheme, being based on variational Bayes estimation. We empirically demonstrate the effectiveness of the proposed framework by using synthetic and real graphs.

Original languageEnglish
Title of host publicationProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
Pages166-171
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland
Duration: 2010 Aug 292010 Sept 1

Publication series

NameProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

Conference

Conference2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
Country/TerritoryFinland
CityKittila
Period10/8/2910/9/1

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