Community structure detection in multipartite networks
| Autor: | Florentin Bota, Mihai Alexandru Suciu, Noémi Gaskó, Rodica Ioana Lung |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Extremal optimization
Fitness function Theoretical computer science business.industry Fitness model Community structure 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Partition (database) Identification (information) Multipartite 020204 information systems 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Artificial intelligence 010306 general physics Heuristics business computer Mathematics |
| Zdroj: | GECCO |
| DOI: | 10.1145/3071178.3071295 |
| Popis: | Community structure detection algorithms are used to identify groups of nodes that are more connected to each other than to the rest of the network. Multipartite networks are a special type of network in which nodes are divided into partitions such that there are no links between nodes in the same partition. However, such nodes may belong to the same community, making the identification of the community structure of a multipartite network computationally challenging. In this paper, we propose a new fitness function that takes into account the information induced by existing links in the network by considering shadowed connections between nodes that have a common neighbor. The existence of a correct fitness function, i.e. one whose optimum values correspond to the community structure of the network, enables the design and use of optimization-based heuristics for solving this problem. We use numerical experiments performed on artificial benchmarks to illustrate the effectiveness of this function used within an extremal optimization based algorithm and compared to existing approaches. As a direct application, a multipartite network constructed from a direct marketing database is analyzed. |
| Databáze: | OpenAIRE |
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