Awesome-Graph-GNN-Recommendation

Graph neural network (GNN), an emerging type of neural network on graph data, has achieved great success on various graph-based tasks and widely used in various scenarios, such as CV, NLP, and recommender systems.

This repository summarizes the related works on GNN and GNN-based recommendation.

GNN: Survey Papers

1. [IEEE TNNLS 2020] A Comprehensive Survey on Graph Neural Networks. paper
2. [IEEE TKDE 2020] Deep Learning on Graphs: A Survey. paper
3. [AI Open] Graph Neural Networks: A Review of Methods and Applications. paper

GNN: Representative Papers

1. [ICLR 2017] Semi-Supervised Classification with Graph Convolutional Networks (GCN). paper
2. [NeurIPS 2017] Inductive representation learning on large graphs (GraphSAGE). paper
3. [ICLR 2018] Graph attention networks (GAT). paper

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1. [ICLR 2014] Spectral Networks and Deep Locally Connected Networks on Graphs. paper
2. [NeurIPS 2016] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. paper
3. [PMLR 2017] Neural message passing for quantum chemistry (MPNN). paper
4. [ICLR 2018] Fastgcn: fast learning with graph convolutional networks via importance sampling (FsatGCN). paper
5. [KDD 2018] Large-scale learnable graph convolutional networks (LGCN). paper
6. [ICLR 2019] How powerful are graph neural networks? (GIN). paper
7. [PMLR 2019] Simplifying graph convolutional networks (SGC). paper

GNN Recommendation: Survey Papers

1. [arXiv 2020] Graph neural networks in recommender systems: a survey. paper
2. [arXiv 2021] Graph learning based recommender systems: A review. paper

GNN Recommendation: Representative Papers

1. [KDD 2018] Graph Convolutional Matrix Completion (GC-MC). paper
2. [KDD 2018] Graph convolutional neural networks for web-scale recommender systems (PinSage). paper
3. [RecSys 2018] Spectral collaborative fltering (SpectralCF). paper
4. [SIGIR 2019] Neural graph collaborative filtering (NGCF). paper
5. [SIGIR 2020] Lightgcn: Simplifying and powering graph convolution network for recommendation (LightGCN). paper

GNN Social Recommendation: Related Papers

1. [SIGIR 2019] A neural influence diffusion model for social recommendation (DiffNet). paper
2. [WWW 2019] Graph neural networks for social recommendation (GraphRec). paper
3. [WWW 2019] Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems (DANSER). paper
4. [RecSys 2019] Deep social collaborative filtering (DSCF). paper
5. [IJCAI 2019] Deep adversarial social recommendation (DASO). paper

GNN Knowledge Graph Recommendation: Related Papers

1. [IEEE TKDE 2020] A Survey on Knowledge Graph-Based Recommender Systems. paper
2. [Information 2021] A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions. paper
3. [WWW 2019] Knowledge graph convolutional networks for recommender systems (KGCN). paper
4. [KDD 2019] Knowledge-aware graph neural networks with label smoothness regularization for recommender systems (KGNN-LS). paper
5. [KDD 2019] Kgat: Knowledge graph attention network for recommendation (KGAT). paper
6. [KDD 2019] Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation (IntentGC). paper

GNN Sequential Recommendation

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other ralated papers:

• [arXiv 2021] Graph Learning: A Survey. paper
• [IEEE Signal Processing Magazine 2017] Geometric deep learning: going beyond euclidean data. paper

other related repositories:

• https://github.com/Jhy1993/Awesome-GNN-Recommendation
• https://github.com/JockWang/Graph-in-Recommendation-Systems
• https://github.com/thunlp/GNNPapers
• https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress