构建可定制的动态社区基准，用于评估社区演化追踪能力。
DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks
- 基于社区中心的模型生成可变演化的社区结构
- 支持社区增减、合并分裂及节点归属变化的动态网络
- 适合评估社区检测与追踪算法在真实演化场景下的表现
图模型有助于理解网络动态与演化。为评估社区检测算法，常采用具有可控拓扑和嵌入社区结构的图。然而，现有基准往往忽略真实网络中社区演化追踪的需求。为此，本文提出一种以社区为中心的新型模型，可生成可定制的动态社区结构，支持社区增长、缩小、合并、分裂、出现或消失。该基准同时生成底层时序网络，其中节点可出现、消失或在社区间迁移。已用该基准测试三种方法，评估其在追踪节点所属社区及检测社区演化方面的性能。提供Python库、绘图工具和验证指标，用于将算法结果与真实标签对比，实现动态社区检测的量化评估。
原文摘要 · Abstract (English)
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.
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