Official code repository for our paper "Deep learning for dynamic graphs: models and benchmarks" accepted at the IEEE Transactions on Neural Networks and Learning Systems.
Please consider citing us
@article{gravina2024benchmark, author={Gravina, Alessio and Bacciu, Davide}, journal={IEEE Transactions on Neural Networks and Learning Systems}, title={{Deep Learning for Dynamic Graphs: Models and Benchmarks}}, year={2024}, volume={}, number={}, pages={1-14}, keywords={Surveys;Representation learning;Benchmark testing;Laplace equations;Graph neural networks;Message passing;Convolution;Benchmark;deep graph networks (DGNs);dynamic graphs;graph neural networks (GNNs);survey;temporal graphs}, doi={10.1109/TNNLS.2024.3379735} } To reproduce the experiments please refer to:
- D-TDG/README.md to reproduce the experiments on the Discrete-Time Dynamic Graph domain.
- C-TDG/README.md to reproduce the experiments on the Continuous-Time Dynamic Graph domain.