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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16877 |
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| _version_ | 1866909301285584896 |
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| author | Brabant, Victor Asgari, Yasaman Borgnat, Pierre Bonifati, Angela Cazabet, Remy |
| author_facet | Brabant, Victor Asgari, Yasaman Borgnat, Pierre Bonifati, Angela Cazabet, Remy |
| contents | Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Longitudinal Modularity, a Modularity for Link Streams Brabant, Victor Asgari, Yasaman Borgnat, Pierre Bonifati, Angela Cazabet, Remy Social and Information Networks Information Retrieval Machine Learning Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation. |
| title | Longitudinal Modularity, a Modularity for Link Streams |
| topic | Social and Information Networks Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2408.16877 |