Supervised Blockmodelling

Leto Peel*

*Corresponding author for this work

Research output: Working paper / PreprintPreprint


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.
Original languageEnglish
PublisherCornell University - arXiv
Volume arXiv:1209.5561v1
Publication statusPublished - 25 Sept 2012
Externally publishedYes


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