https://github.com/gravins/dynamic_graph_benchmark

The official repository for the paper "Deep learning for dynamic graphs: models and benchmarks" accepted at IEEE TNNLS
https://github.com/gravins/dynamic_graph_benchmark

benchmark dynamic-graphs graph-neural-networks graphneuralnetwork

Last synced: about 2 months ago
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The official repository for the paper "Deep learning for dynamic graphs: models and benchmarks" accepted at IEEE TNNLS

• Host: GitHub
• URL: https://github.com/gravins/dynamic_graph_benchmark
• Owner: gravins
• Created: 2023-07-03T11:52:17.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-10T09:37:45.000Z (6 months ago)
• Last Synced: 2024-07-10T11:34:32.372Z (6 months ago)
• Topics: benchmark, dynamic-graphs, graph-neural-networks, graphneuralnetwork
• Language: Python
• Homepage: https://ieeexplore.ieee.org/document/10490120
• Size: 615 KB
• Stars: 11
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Deep learning for dynamic graphs: models and benchmarks

Official code repository for our paper [***"Deep learning for dynamic graphs: models and benchmarks"***](https://ieeexplore.ieee.org/document/10490120) 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}

	}

## How to run the experiments

To reproduce the experiments please refer to:

- [D-TDG/README.md](https://github.com/gravins/dynamic_graph_benchmark/tree/main/D-TDG) to reproduce the experiments on the *Discrete-Time Dynamic Graph* domain. 

- [C-TDG/README.md](https://github.com/gravins/dynamic_graph_benchmark/tree/main/C-TDG) to reproduce the experiments on the *Continuous-Time Dynamic Graph* domain.