# Longitudinal Modularity, a Modularity for Link Streams

arXiv: https://arxiv.org/abs/2408.16877 · HTML: https://mlsanity.com/paper/2408.16877/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Stream Graphs and Link Streams for the Modeling of Interactions over Time](https://mlsanity.com/paper/1710.04073/) (2017)
- [A semiparametric extension of the stochastic block model for longitudinal networks](https://mlsanity.com/paper/1512.07075/) (2015)
- [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)
- [Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path structures and centralities](https://mlsanity.com/paper/1508.06467/) (2015)
- [Evaluating Community Detection Algorithms for Progressively Evolving Graphs](https://mlsanity.com/paper/2007.08635/) (2020)
- [Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths](https://mlsanity.com/paper/2112.00183/) (2021)
- [OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks](https://mlsanity.com/paper/1804.03842/) (2018)
- [Non-Altering Time Scales for Aggregation of Dynamic Networks into Series of Graphs](https://mlsanity.com/paper/1805.06188/) (2018)
- [Detecting Stable Communities in Link Streams at Multiple Temporal Scales](https://mlsanity.com/paper/1907.10453/) (2019)
- [Computing maximal cliques in link streams](https://mlsanity.com/paper/1502.00993/) (2015)
- [Comparison of modularity-based approaches for nodes clustering in hypergraphs](https://mlsanity.com/paper/2401.14028/) (2024)

## Built on by (most cited first, 2 of 2)

- [Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity](https://mlsanity.com/paper/2510.00741/) (2025)
- [Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph](https://mlsanity.com/paper/2608.03695/) (2026)

## Abstract (arXiv)

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.