Image constrained blockmodelling: a constraint programming approach

Mohadeseh Ganji, Jeffrey Chan, Peter J. Stuckey, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Ian Davidson

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

6 Citations (Scopus)


Blockmodelling is an important technique for detecting un-derlying patterns in graphs. However, existing blockmod-elling algorithms do not provide the user with any explicit control to specify which patterns might be of interest. Fur-thermore, existing algorithms focus on finding standard com-munity structures in graphs, and are likely to overlook in-formative but more complex patterns, such as hierarchical or ring blockmodel structures. In this paper, we propose a generic constraint programming framework for blockmod-elling, which allows a user to specify and search for complex blockmodel patterns in graphs. Our proposed framework can be incorporated into existing iterative blockmodelling algorithms, operating as a hybrid optimization scheme that provides high exibility and expressiveness. We demonstrate the power of our framework for discovering complex pat-terns, via experiments over a range of synthetic and real data sets.

Original languageEnglish
Title of host publicationProceedings of the 2018 SIAM International Conference on Data Mining (SDM18)
Subtitle of host publicationSan Diego, California, USA
EditorsMartin Ester, Dino Pedreschi
Place of PublicationPhiladelphia PA USA
PublisherSociety for Industrial & Applied Mathematics (SIAM)
Number of pages9
ISBN (Electronic)9781611975321
Publication statusPublished - 2018
Externally publishedYes
EventSIAM International Conference on Data Mining 2018 - San Diego Marriott Mission Valley, San Diego, United States of America
Duration: 3 May 20185 May 2018


ConferenceSIAM International Conference on Data Mining 2018
Abbreviated titleSDM 18
CountryUnited States of America
CitySan Diego
Internet address


  • Blockmodelling
  • Constraint Programming

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