# Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity

arXiv: https://arxiv.org/abs/2510.00741 · HTML: https://mlsanity.com/paper/2510.00741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Statistical clustering of temporal networks through a dynamic stochastic block model](https://mlsanity.com/paper/1506.07464/) (2015)
- [Stream Graphs and Link Streams for the Modeling of Interactions over Time](https://mlsanity.com/paper/1710.04073/) (2017)
- [Community Discovery in Dynamic Networks: a Survey](https://mlsanity.com/paper/1707.03186/) (2017)
- [Flow stability for dynamic community detection](https://mlsanity.com/paper/2101.06131/) (2021)
- [A Generalized and Adaptive Method for Community Detection](https://mlsanity.com/paper/1406.2518/) (2014)
- [Mosaic benchmark networks: Modular link streams for testing dynamic community detection algorithms](https://mlsanity.com/paper/2310.02840/) (2023)
- [Longitudinal Modularity, a Modularity for Link Streams](https://mlsanity.com/paper/2408.16877/) (2024)
- [Constrained information flows in temporal networks reveal intermittent communities](https://mlsanity.com/paper/1711.07649/) (2017)

## Abstract (arXiv)

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.