Overview of Research Papers and Resources for Graph Neural Networks and Graph Machine Learning
- Zhou, Jie, et al. "Graph neural networks: A review of methods and applications." AI open 1 (2020): 57-81.
- Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE transactions on neural networks and learning systems 32.1 (2020): 4-24.
- Jiang, Weiwei, and Jiayun Luo. "Graph neural network for traffic forecasting: A survey." Expert Systems with Applications (2022): 117921. Wu, Lingfei, et al. "Graph neural networks: foundation, frontiers and applications." Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022.
- Stamile, Claudio, Aldo Marzullo, and Enrico Deusebio. Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms. Packt Publishing Ltd, 2021.
- Hamilton, William L. Graph Representation Learning. San Rafael Morgan Et Claypool, 2020.
- Leskovec, Jure. “CS224W Machine Learning with Graphs | Home.” Web.stanford.edu, 2022, web.stanford.edu/class/cs224w/.
| Library Name | Licence | Github Stars | Frameworks |
|---|---|---|---|
| Pytorch Geometric | MIT | 17.6k | Python, PyTorch |
| Deep Graph Library | Apache 2.0 | 11.7k | Python, PyTorch, TF, MxNet |
| Graph Nets | Apache 2.0 | 5.3k | Python, PyTorch |
| Spektral | MIT | 2.3k | Python, TF2/Keras |
| Dive into Graphs | GNU v3.0 | 1.5k | Python, PyTorch |
| Jraph | Apache 2.0 | 1.2k | Jax, PyTorch |
| Comprehensive Library for Graph Deep Learning | MIT | 1.5k | Python, PyTorch |
| Name | Licence | Github Stars | Frameworks | Purpose |
|---|---|---|---|---|
| Networkx | BSD-3 | 9.9k | Python | Graphs, Visualisation algorithms |
| PyGSP | BSD-3 | 1.4k | Python | Signal processing on Graphs |
| PyTorch-BigGraph | MIT | 4.4k | PyTorch | Building large-scale graph embeddings |
| Gephi | GPL-3.0 | 7.2k | Java | Graph Visualisation |
| Pyvis | MIT | 3.3k | Python | Graph Visualisation |
| netrd | - | - | Python | Network reconstruction, graph distances |