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

2025-10-03

AI Summary

Existing dynamic network benchmarks largely neglect community evolution modeling, thus failing to adequately evaluate algorithms’ capability to capture realistic community lifecycles (e.g., growth, contraction, splitting, merging, dissolution) and node-level dynamics (e.g., emergence, disappearance, inter-community migration). Method: We propose a community-centric temporal network generation model that—uniquely—couples community evolution with node dynamics, enabling fine-grained specification of ground-truth community trajectories. Leveraging stochastic graph mechanisms and lifecycle-aware control policies, we construct a scalable benchmark suite, accompanied by standardized evaluation metrics and interactive visualization tools. Contribution/Results: Extensive experiments demonstrate the benchmark’s sensitivity to three state-of-the-art dynamic community detection algorithms, significantly enhancing quantitative assessment of evolutionary behavior recognition and member trajectory tracking. Our work fills a critical gap in dynamic community tracking evaluation, providing the first principled, configurable, and empirically validated benchmark for this task.

Technology Category

Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityPlanning, Routing, and Scheduling: Temporal PlanningKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsWeb Mining and Content Analysis: Models for Web evolutionUser Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

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.

Problem

• Benchmarking community detection and tracking in temporal networks
• Generating customizable evolving community structures with dynamic changes
• Evaluating algorithm performance in tracking node membership and community evolution

Innovation

• Generates customizable evolving community structures
• Produces underlying temporal network with node dynamics
• Provides validation metrics to compare algorithm results