Temporal Graph Benchmark

Temporal graph benchmarks are standardized datasets and evaluation frameworks designed to rigorously assess machine learning models' ability to analyze and predict patterns in dynamic, evolving networks. Current research focuses on developing and evaluating models, including those based on neural common neighbors, state space models, and evolving Fourier transforms, to improve link prediction and node property prediction accuracy on large-scale temporal graphs. The availability of robust benchmarks like TGB facilitates more reproducible and comparable research, ultimately advancing the field of temporal graph analysis and its applications in diverse areas such as social network analysis and financial modeling. Challenges remain in handling the computational complexity of large datasets and addressing limitations of current models in capturing global temporal dynamics.