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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)

Abstract

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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More Information

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

Identifiers

Item ID: 14956
URI: http://sure.sunderland.ac.uk/id/eprint/14956
Official URL: https://www.sheffield.ac.uk/ukci2022

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

Catalogue record

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

Contributors

Author: Kenneth McGarry ORCID iD
Author: Kenneth McGarry

University Divisions

Faculty of Technology

Subjects

Computing > Data Science
Computing > Artificial Intelligence
Computing

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