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| Content Provider | ACM Digital Library |
|---|---|
| Author | Gaskó, Noémi Bota, Florentin Suciu, Mihai Lung, Rodica Ioana |
| Abstract | 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. |
| Starting Page | 259 |
| Ending Page | 265 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450349208 |
| DOI | 10.1145/3071178.3071295 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-07-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Community structure Multipartite networks Fitness function |
| Content Type | Text |
| Resource Type | Article |
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