Graph Generator

Graph generators are algorithms designed to create synthetic graphs with specific properties, serving purposes such as testing graph algorithms, training graph neural networks (GNNs), and generating counterfactual explanations for GNN decisions. Current research emphasizes generating structurally diverse graphs, incorporating graph structure into the generation process for data condensation, and creating graphs that match the distribution of real-world data for improved GNN explainability and fairness. These advancements are crucial for addressing challenges in GNN training, improving model interpretability, and enabling rigorous evaluation of graph algorithms and fairness in machine learning applications.