Graph Benchmark

Graph benchmarks are standardized datasets and evaluation protocols used to assess the performance of graph neural networks (GNNs) and other graph learning algorithms. Current research focuses on developing benchmarks that address challenges like heterophily (nodes with dissimilar neighbors), heterogeneity (diverse node types and edge relations), dynamic graphs (evolving structure and attributes), and long-range dependencies, often employing architectures such as graph transformers and message-passing neural networks with various enhancements. These benchmarks are crucial for advancing the field by enabling fair comparisons of different algorithms, identifying limitations of existing methods, and ultimately driving the development of more robust and effective graph learning techniques for diverse real-world applications.