Image constrained blockmodelling: A constraint programming approach

Blockmodelling is an important technique for detecting underlying patterns in graphs. However, existing blockmodelling algorithms do not provide the user with any explicit control to specify which patterns might be of interest. Furthermore, existing algorithms focus on finding standard community structures in graphs, and are likely to overlook informative but more complex patterns, such as hierarchical or ring blockmodel structures. In this paper, we propose a generic constraint programming framework for blockmodelling, 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 flexibility and expressiveness. We demonstrate the power of our framework for discovering complex patterns, via experiments over a range of synthetic and real data sets.