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Algorithms 2016, 9(1), 21; doi:10.3390/a9010021

Multivariate Algorithmics for Finding Cohesive Subnetworks

Institut für Informatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany
Academic Editor: Henning Fernau
Received: 18 January 2016 / Revised: 8 March 2016 / Accepted: 9 March 2016 / Published: 16 March 2016
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Abstract

Community detection is an important task in the analysis of biological, social or technical networks. We survey different models of cohesive graphs, commonly referred to as clique relaxations, that are used in the detection of network communities. For each clique relaxation, we give an overview of basic model properties and of the complexity of the problem of finding large cohesive subgraphs under this model. Since this problem is usually NP-hard, we focus on combinatorial fixed-parameter algorithms exploiting typical structural properties of input networks. View Full-Text
Keywords: clique relaxations; dense subgraphs; NP-hard problem; network communities; fixed-parameter algorithms clique relaxations; dense subgraphs; NP-hard problem; network communities; fixed-parameter algorithms
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Komusiewicz, C. Multivariate Algorithmics for Finding Cohesive Subnetworks. Algorithms 2016, 9, 21.

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