# DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks

arXiv: https://arxiv.org/abs/2510.06245 · HTML: https://mlsanity.com/paper/2510.06245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [The ground truth about metadata and community detection in networks](https://mlsanity.com/paper/1608.05878/) (2016)
- [Mosaic benchmark networks: Modular link streams for testing dynamic community detection algorithms](https://mlsanity.com/paper/2310.02840/) (2023)
- [Benchmark model to assess community structure in evolving networks](https://mlsanity.com/paper/1501.05808/) (2015)

## Abstract (arXiv)

Graph models help understand network dynamics and evolution. Creating graphs with controlled topology and embedded partitions is a common strategy for evaluating community detection algorithms. However, existing benchmarks often overlook the need to track the evolution of communities in real-world networks. To address this, a new community-centered model is proposed to generate customizable evolving community structures where communities can grow, shrink, merge, split, appear or disappear. This benchmark also generates the underlying temporal network, where nodes can appear, disappear, or move between communities. The benchmark has been used to test three methods, measuring their performance in tracking nodes' cluster membership and detecting community evolution. Python libraries, drawing utilities, and validation metrics are provided to compare ground truth with algorithm results for detecting dynamic communities.