Quantifying community evolution in temporal networks
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915624542797824 |
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| author | Zhong, Peijie Ba, Cheick Mondragón, Raúl Clegg, Richard |
| author_facet | Zhong, Peijie Ba, Cheick Mondragón, Raúl Clegg, Richard |
| contents | When we detect communities in temporal networks it is important to ask questions about how they change in time. Normalised Mutual Information (NMI) has been used to measure the similarity of communities when the nodes on a network do not change. We propose two extensions namely Union-Normalised Mutual Information (UNMI) and Intersection-Normalised Mutual Information (INMI). UNMI and INMI evaluate the similarity of community structure under the condition of node variation. Experiments show that these methods are effective in dealing with temporal networks with the changes in the set of nodes, and can capture the dynamic evolution of community structure in both synthetic and real temporal networks. This study not only provides a new similarity measurement method for network analysis but also helps to deepen the understanding of community change in complex temporal networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10632 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying community evolution in temporal networks Zhong, Peijie Ba, Cheick Mondragón, Raúl Clegg, Richard Social and Information Networks When we detect communities in temporal networks it is important to ask questions about how they change in time. Normalised Mutual Information (NMI) has been used to measure the similarity of communities when the nodes on a network do not change. We propose two extensions namely Union-Normalised Mutual Information (UNMI) and Intersection-Normalised Mutual Information (INMI). UNMI and INMI evaluate the similarity of community structure under the condition of node variation. Experiments show that these methods are effective in dealing with temporal networks with the changes in the set of nodes, and can capture the dynamic evolution of community structure in both synthetic and real temporal networks. This study not only provides a new similarity measurement method for network analysis but also helps to deepen the understanding of community change in complex temporal networks. |
| title | Quantifying community evolution in temporal networks |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2411.10632 |