Abstract
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 language | English |
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Title of host publication | Proceedings of the 2018 SIAM International Conference on Data Mining (SDM18) |
Subtitle of host publication | San Diego, California, USA |
Editors | Martin Ester, Dino Pedreschi |
Place of Publication | Philadelphia PA USA |
Publisher | Society for Industrial & Applied Mathematics (SIAM) |
Pages | 19-27 |
Number of pages | 9 |
ISBN (Electronic) | 9781611975321 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | SIAM International Conference on Data Mining 2018 - San Diego Marriott Mission Valley, San Diego, United States of America Duration: 3 May 2018 → 5 May 2018 https://epubs.siam.org/doi/10.1137/1.9781611975321.fm |
Conference
Conference | SIAM International Conference on Data Mining 2018 |
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Abbreviated title | SDM 18 |
Country | United States of America |
City | San Diego |
Period | 3/05/18 → 5/05/18 |
Internet address |
Keywords
- Blockmodelling
- Constraint Programming