A Generalized Modularity for Computing Community Structure in Fully Signed Networks
The community structure in fully signed networks that considers both node attributes and edge signs is important in computational social science; however, its physical description still requires further exploration, and the corresponding measurement remains lacking. In this paper, we present a gener...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/8767131 |
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| Summary: | The community structure in fully signed networks that considers both node attributes and edge signs is important in computational social science; however, its physical description still requires further exploration, and the corresponding measurement remains lacking. In this paper, we present a generalized framework of community structure in fully signed networks, based on which a variant of modularity is designed. An optimization algorithm that maximizes modularity to detect potential communities is also proposed. Experiments show that the proposed method can efficiently optimize the objective function and perform effective community detection. |
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| ISSN: | 1099-0526 |