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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| Published in: | Algorithms Vol. 9; no. 1; p. 21 |
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| Format: | Journal Article |
| Language: | English |
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01.03.2016
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| ISSN: | 1999-4893, 1999-4893 |
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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. |
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| AbstractList | 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. |
| Author | Komusiewicz, Christian |
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| SubjectTerms | clique relaxations dense subgraphs fixed-parameter algorithms network communities NP-hard problem |
| Title | Multivariate Algorithmics for Finding Cohesive Subnetworks |
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