Supervised Blockmodelling

TL;DR

This paper introduces supervised blockmodels that leverage link structure for classification, capturing complex interaction patterns and providing interpretable summaries of network interactions.

Contribution

It presents three variants of supervised blockmodels that improve classification and interpretability by modeling diverse interaction patterns in networks.

Findings

Supervised blockmodels achieve high classification accuracy.

Models effectively capture assortative and disassortative interactions.

The approach provides interpretable summaries of network structures.

Abstract

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.