Supervised Blockmodelling
This work addresses the need for more flexible and interpretable models in network analysis, though it appears incremental as it explores variants of existing blockmodel approaches.
The paper tackled the problem of collective classification by introducing supervised blockmodels that learn patterns of interactions between classes without relying on assortativity assumptions, achieving good classification performance using link structure alone and providing interpretable network summaries.
Collective classification models attempt to improve classification performance by taking into account the class labels of related instances. However, they tend not to learn patterns of interactions between classes and/or make the assumption that instances of the same class link to each other (assortativity assumption). Blockmodels provide a solution to these issues, being capable of modelling assortative and disassortative interactions, and learning the pattern of interactions in the form of a summary network. The Supervised Blockmodel provides good classification performance using link structure alone, whilst simultaneously providing an interpretable summary of network interactions to allow a better understanding of the data. This work explores three variants of supervised blockmodels of varying complexity and tests them on four structurally different real world networks.