GNN Papers, Library

gnn_paper_library.md

1. GNN Papers

1) Survey

• Graph Neural Networks: A Review of Methods and Applications, arxiv 2018.
• A Comprehensive Survey on Graph Neural Networks, arxiv 2019.
• A Gentle Introduction to Deep Learning for Graphs.

2) Algorithms

• Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017. (GCN)
• Inductive representation learning on large graphs (GraphSAGE), NIPS 2017
• Graph Attention Networks (GAT), ICLR 2018.
• How Powerful are Graph Neural Networks?, ICLR 2019.(GIN)

3) Analysis

• Benchmarking Graph Neural Networks, arxiv 2020.
• A Fair Comparison of Graph Neural Networks for Graph Classification, ICLR 2020.

4) GNN with Recommender System

• Graph Convolutional Matrix Completion (GCMC), arxiv 2017.
• Neural Graph Collaborative Filtering, SIGIR 2019.
• KGAT: Knowledge Graph Attention Network for Recommendation, KDD 2019.
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, SIGIR 2020.
• Session-based Recommendation with Graph Neural Networks, AAAI 2019.

5) Others

• Gated Graph Sequence Neural Networks, ICLR 2016.
• Graph-to-Sequence Learning using Gated Graph Neural Networks, ACL 2018.
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training, KDD 2020.
• GPT-GNN: Generative Pre-Training of Graph Neural Networks, KDD 2020.

2. Library

• Deep Graph Library (DGL)
• PyTorch Geometric