- GAT : https://arxiv.org/pdf/1710.10903.pdf
- Heterogeneous Graph Attention Network Xiao Wang et al. : https://arxiv.org/pdf/1903.07293v1.pdf
- GCMC: https://arxiv.org/pdf/1706.02263.pdf
- GraphSAGE : https://arxiv.org/pdf/1706.02216.pdf
- PinSAGE: https://arxiv.org/pdf/1806.01973.pdf
- Multi-Component Graph Convolutional Collaborative Filtering : https://ojs.aaai.org/index.php/AAAI/article/view/6094
- Diffnet++: https://arxiv.org/pdf/2002.00844.pdf
- GraphRec: https://arxiv.org/abs/1902.07243
- Dynamic GNN for Seq. Rec: https://arxiv.org/pdf/2104.07368.pdf
- MAGNN: https://arxiv.org/pdf/2002.01680.pdf
- Link prediction based on GNNs : https://arxiv.org/pdf/1802.09691.pdf
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges : https://arxiv.org/abs/2104.13478
- GCN : https://arxiv.org/pdf/1609.02907.pdf
- https://www.sciencedirect.com/science/article/pii/S2666651021000012?via%3Dihub
- Graph Neural Networks in Recommender Systems: A Survey(https://arxiv.org/pdf/2011.02260.pdf)
- A Comprehensive Survey on Graph Neural Networks(https://ieeexplore.ieee.org/abstract/document/9046288)
- A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions : https://arxiv.org/pdf/2109.12843v2.pdf
- code from articles : https://github.com/linafaik08/graph_neural_networks/tree/main
- pytorch geometric : https://pytorch-geometric.readthedocs.io/en/latest/Cx2i8an--Tkt7O8Z , https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html
- GCN : https://github.com/pyg-team/pytorch_geometric/blob/master/examples/gcn.py
- GAT : https://github.com/gordicaleksa/pytorch-GAT/blob/main/models/definitions/GAT.py#L53 , https://github.com/PetarV-/GAT/blob/master/models/gat.py
- https://github.com/pyg-team/pytorch_geometric
- package for scraping GitHub data: https://pypi.org/project/scrape-up/
- https://towardsdatascience.com/graph-neural-network-gnn-architectures-for-recommendation-systems-7b9dd0de0856
- https://medium.com/data-from-the-trenches/graphical-neural-networks-link-prediction-part-ii-c60f6d97fd97
- Transformers are Graph Neural Networks : https://thegradient.pub/transformers-are-graph-neural-networks/
- https://www.kaggle.com/code/validmodel/graph-neural-network-starter-for-beginners
- book : https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf?trk=public_post_comment-text
- GNN playlist : https://youtube.com/playlist?list=PLV8yxwGOxvvoNkzPf
- AMMI Geometric Deep Learning Course - First Edition (2021) : https://youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3
- ICLR keynote 2021(GNN) : https://www.youtube.com/watch?v=w6Pw4MOzMuo&t=334s
- Theoretical Foundations of Graph Neural Networks by Petar Veličković : https://youtu.be/uF53xsT7mjc
- graphsage in uber eats recommendations : https://www.uber.com/en-IN/blog/uber-eats-graph-learning/ , https://www.youtube.com/live/9O9osybNvyY?feature=share
- https://youtu.be/Qip-Cgv1cMU
- https://youtube.com/playlist?list=PLBoQnSflObckArGNhOcNg7lQG_f0ZlHF5
- ML with graphs playlist : https://youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn
- GNNs Beyond Permutation Equivariance video : https://youtu.be/aCUOAkOqNoU
- https://geometricdeeplearning.com/
- GNN recommender systems repo : https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems
- https://github.com/thunlp/GNNPapers
- https://github.com/safe-graph/graph-fraud-detection-papers
- https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems#Recommendation-Stages
- twitter thread of resources: https://twitter.com/PetarV_93/status/1306689702020382720
- Articles on GNNs: https://towardsdatascience.com/graph-deep-learning/home
- https://petar-v.com/GAT/
- GFI-Bot: Automated Good First Issue Recommendation on GitHub: https://hehao98.github.io/files/2022-gfibot.pdf
- Graph fraud detection papers: https://github.com/safe-graph/graph-fraud-detection-papers
- Benchmarking Graph Neural Networks: https://arxiv.org/pdf/2003.00982.pdf
- Linear Algebra : https://youtube.com/playlist?list=PL221E2BBF13BECF6C
- Multi variable calculus : https://www.khanacademy.org/math/multivariable-calculus
- Statistics : https://youtube.com/playlist?list=PLUl4u3cNGP61MdtwGTqZA0MreSaDybji8
- ML techniques course : https://youtube.com/playlist?list=PLZ2ps__7DhBbA_e6_G3FI-BA1f7lCINUu
- NN and DL book : http://neuralnetworksanddeeplearning.com/chap1.html
- above book's solutions : https://nbviewer.org/github/nndl-solutions/NNDL-solutions/blob/master/notebooks/chap-1-using-neural-nets-to-recognize-handwritten-digits.ipynb
- deep learning book : https://www.deeplearningbook.org/
- practical course(DL) : https://youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU
- theoretical course(DL) : https://youtube.com/playlist?list=PL3pGy4HtqwD2kwldm81pszxZDJANK3uGV
- CF model for books RS : https://youtu.be/1YoD0fg3_EM (code : https://github.com/campusx-official/book-recommender-system/blob/master/book-recommender-system.ipynb)
- video : https://youtu.be/RVJV8VGa1ZY
- Collaborative and content based filtering video : https://youtu.be/v90un9ALRzw
- Neural Collaborative Filtering recommendation model : https://youtu.be/_uhU7s9kOEM