DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks
This provides a tool for researchers in network analysis to evaluate community detection and tracking algorithms, though it is incremental as it builds on existing benchmarking approaches.
The paper tackles the lack of benchmarks for tracking community evolution in temporal networks by proposing DynBenchmark, a customizable model that generates evolving community structures and underlying networks, and it was used to test three methods for performance in tracking cluster membership and detecting evolution.
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.