Semi-supervised Blockmodelling with Pairwise Guidance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)


Blockmodelling is an important technique for detecting underlying patterns in graphs. Existing blockmodelling algorithms are unsupervised and cannot take advantage of the existing information that might be available about objects that are known to be similar. This background information can help finding complex patterns, such as hierarchical or ring blockmodel structures, which are difficult for traditional blockmodelling algorithms to detect. In this paper, we propose a new semi-supervised framework for blockmodelling, which allows background information to be incorporated in the form of pairwise membership information. Our proposed framework is based on the use of Lagrange multipliers and can be incorporated into existing iterative blockmodelling algorithms, enabling them to find complex blockmodel patterns in graphs. We demonstrate the utility of our framework for discovering complex patterns, via experiments over a range of synthetic and real data sets. Code related to this paper is available at:


Blockmodelling Pairwise information Lagrange multipliers 

Supplementary material

478890_1_En_10_MOESM1_ESM.pdf (85 kb)
Supplementary material 1 (pdf 84 KB)


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia
  2. 2.School of Computer Science and Software EngineeringRMIT UniversityMelbourneAustralia
  3. 3.Computer Science DepartmentWestern Sydney UniversityPenrithAustralia

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