Multivariate Algorithmics for Finding Cohesive Subnetworks

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 b...

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Veröffentlicht in:Algorithms Jg. 9; H. 1; S. 21
1. Verfasser: Komusiewicz, Christian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: MDPI AG 01.03.2016
Schlagworte:
clique relaxations
dense subgraphs
fixed-parameter algorithms
network communities
NP-hard problem
ISSN:1999-4893, 1999-4893
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Zusammenfassung: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.
ISSN:1999-4893
1999-4893
DOI:10.3390/a9010021