Synthetic Graph

Synthetic graph generation is a rapidly developing field focused on creating artificial graphs that mimic the properties of real-world networks, addressing data scarcity and privacy concerns in graph-based machine learning. Current research emphasizes developing efficient algorithms for generating large, attributed graphs with realistic structural features (e.g., degree distributions, community structures) and employing these graphs for benchmarking graph neural networks (GNNs), improving GNN training, and enabling privacy-preserving data sharing. This work is crucial for advancing graph-based machine learning, providing reliable benchmarks for algorithm evaluation, and facilitating the analysis of sensitive network data while protecting privacy.