# ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection

arXiv: https://arxiv.org/abs/2608.16906 · HTML: https://mlsanity.com/paper/2608.16906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 54 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 20 of 20)

- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization](https://mlsanity.com/paper/2312.12697/) (2023)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [Flow stability for dynamic community detection](https://mlsanity.com/paper/2101.06131/) (2021)
- [Spectral Subspace Clustering for Attributed Graphs](https://mlsanity.com/paper/2411.11074/) (2024)
- [Parallel Heuristics for Scalable Community Detection](https://mlsanity.com/paper/1410.1237/) (2014)
- [Recent Advances in Fully Dynamic Graph Algorithms](https://mlsanity.com/paper/2102.11169/) (2021)
- [Large network community detection by fast label propagation](https://mlsanity.com/paper/2209.13338/) (2022)
- [Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation](https://mlsanity.com/paper/2405.20277/) (2024)
- [DynaMo: Dynamic Community Detection by Incrementally Maximizing Modularity](https://mlsanity.com/paper/1709.08350/) (2017)
- [GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting](https://mlsanity.com/paper/2312.13936/) (2023)
- [Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis](https://mlsanity.com/paper/2401.03194/) (2024)
- [LabelRankT: Incremental Community Detection in Dynamic Networks via Label Propagation](https://mlsanity.com/paper/1305.2006/) (2013)
- [A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks](https://mlsanity.com/paper/1407.2683/) (2014)
- [OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks](https://mlsanity.com/paper/1804.03842/) (2018)
- [Random walk based snapshot clustering for detecting community dynamics in temporal networks](https://mlsanity.com/paper/2412.12187/) (2024)
- [Community Detection in Dynamic Networks via Adaptive Label Propagation](https://mlsanity.com/paper/1711.06535/) (2017)
- [A Starting Point for Dynamic Community Detection with Leiden Algorithm](https://mlsanity.com/paper/2405.11658/) (2024)

## Abstract (arXiv)

Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the semantics of mature static solvers, but it repeatedly processes unchanged graph regions when updates are small. Solver-specific dynamic methods can reduce this cost, but their update rules often have limited transferability across objectives, feature representations, and implementations. In addition, localizing computation only by graph distance may omit community context needed by high-quality solvers. We introduce ComNetX, a solver-agnostic hierarchical adaptation framework for local dynamic updates. ComNetX maintains a multi-level community state, expands the updated region, closes it over affected communities, and contracts these communities into compact local instances. This affected-community closure and contraction preserve solver context while restricting computation to the changed part of the graph. The same interface can wrap modularity heuristics, graph-clustering models that use node features, and native dynamic solvers as local backends. We evaluate ComNetX through a multi-backend study on six real networks, longer real-data streams for topology-based backends, and controlled dynamic stochastic block model stress streams. The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup. The combined protocols also identify regimes where locality breaks down and a full refresh is preferable.