This repository provides a standardized benchmarking framework for Graph Neural Networks (GNNs), enabling reproducible evaluation of different GNN architectures on tasks including molecular property prediction (ZINC, AQSOL), graph classification (PATTERN, CLUSTER), and mathematical graph theory problems. The framework uses DGL (Deep Graph Library) and PyTorch as backend, supports both CPU and GPU environments, and includes datasets and leaderboards for comparing model performance.