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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Graph theoretic and stochastic block models integrated with matrix factorization for community detection

McGarry, Kenneth (2022) Graph theoretic and stochastic block models integrated with matrix factorization for community detection. In: The 21st UK Workshop on Computational Intelligence, 7 - 9 September 2022, University of Sheffield.. (In Press)

Item Type: Conference or Workshop Item (Paper)


In this work we describe a novel method to integrate graph theoretic and stochastic block models by using matrix factorization for the purposes of data mining interesting patterns. Complex networks represent pairwise patterns of connectivity between nodes and can reveal much information terms of the relationships between entities. Further information on these relationships can be extracted through a careful analysis of the shared communities they coexist with. Here we use the strengths of stochastic block models which are widely used for community detection and are a natural extension of complex networks. However, numerous false positive community affiliations are often identified. We integrate the two types of network with a non negative matrix factorization function. We test and validate our methods against other competing systems on several data sets.

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Uncontrolled Keywords: Stochastic block model, subgraph, non-negative matrix factorization, complex networ
Depositing User: Kenneth McGarry


Item ID: 14956
Official URL:

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ORCID for Kenneth McGarry: ORCID iD

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Date Deposited: 01 Aug 2022 10:26
Last Modified: 01 Aug 2022 10:26


Author: Kenneth McGarry ORCID iD

University Divisions

Faculty of Technology


Computing > Data Science
Computing > Artificial Intelligence

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