Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911187741966336 |
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| author | Brabant, Victor Bonifati, Angela Cazabet, Rémy |
| author_facet | Brabant, Victor Bonifati, Angela Cazabet, Rémy |
| contents | Community detection is a fundamental problem in network analysis, with many applications in various fields. Extending community detection to the temporal setting with exact temporal accuracy, as required by real-world dynamic data, necessitates methods specifically adapted to the temporal nature of interactions. We introduce LAGO, a novel method for uncovering dynamic communities by greedy optimization of Longitudinal Modularity, a specific adaptation of Modularity for continuous-time networks. Unlike prior approaches that rely on time discretization or assume rigid community evolution, LAGO captures the precise moments when nodes enter and exit communities. We evaluate LAGO on synthetic benchmarks and real-world datasets, demonstrating its ability to efficiently uncover temporally and topologically coherent communities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity Brabant, Victor Bonifati, Angela Cazabet, Rémy Social and Information Networks Machine Learning Community detection is a fundamental problem in network analysis, with many applications in various fields. Extending community detection to the temporal setting with exact temporal accuracy, as required by real-world dynamic data, necessitates methods specifically adapted to the temporal nature of interactions. We introduce LAGO, a novel method for uncovering dynamic communities by greedy optimization of Longitudinal Modularity, a specific adaptation of Modularity for continuous-time networks. Unlike prior approaches that rely on time discretization or assume rigid community evolution, LAGO captures the precise moments when nodes enter and exit communities. We evaluate LAGO on synthetic benchmarks and real-world datasets, demonstrating its ability to efficiently uncover temporally and topologically coherent communities. |
| title | Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2510.00741 |