tonylv/awesome-gnn-recommendation

Graph Neural Network

[TOC]

Graph Neural Network

Dynamic Graph

1. Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution. AAAI 2018
2. Dynamic Network Embedding by Modeling Triadic Closure Process. AAAI 2018
3. DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI 2018
4. A Generative Model for Dynamic Networks with Applications. AAAI 2019
5. Communication-optimal distributed dynamic graph clustering. AAAI 2019
6. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. AAAI 2020
7. Dynamic Network Pruning with Interpretable Layerwise Channel Selection. AAAI 2020
8. DyRep: Learning Representations over Dynamic Graphs. ICLR 2019
9. Dynamic Graph Representation Learning via Self-Attention Networks. ICLR 2019
10. The Logical Expressiveness of Graph Neural Networks. ICLR 2020
11. Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees. WWW 2018
12. Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding. IJCAI 2018
13. Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks. IJCAI 2018
14. AddGraph: Anomaly Detection in Dynamic Graph using Attention-based Temporal GCN. IJCAI 2019
15. Network Embedding and Change Modeling in Dynamic Heterogeneous Networks. SIGIR 2019
16. Learning Dynamic Node Representations with Graph Neural Networks. SIGIR 2020
17. Dynamic Link Prediction by Integrating Node Vector Evolution and Local Neighborhood Representation. SIGIR 2020
18. NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. KDD 2018
19. Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. KDD 2019
20. Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. KDD 2019
21. Laplacian Change Point Detection for Dynamic Graphs. KDD 2020
22. Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction Neural Dynamics on Complex Networks KDD 2020
23. Fast Approximate Spectral Clustering for Dynamic Networks. ICML 2018
24. Improved Dynamic Graph Learning through Fault-Tolerant Sparsification. ICML 2019
25. Efficient SimRank Tracking in Dynamic Graphs. ICDE 2018
26. On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs. ICDE 2018
27. Computing a Near-Maximum Independent Set in Dynamic Graphs. ICDE 2019
28. Finding Densest Lasting Subgraphs in Dynamic Graphs: A Stochastic Approach. ICDE 2019
29. Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks. ICDE 2019
30. Adaptive Dynamic Bipartite Graph Matching: A Reinforcement Learning Approach. ICDE 2019
31. A Fast Sketch Method for Mining User Similarities Over Fully Dynamic Graph Streams.

Heterogeneous Graph

1. Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun. Heterogeneous Graph Transformer. WWW 2020

2. Yuxiang Ren and Bo Liu and Chao Huang and Peng Dai and Liefeng Bo and Jiawei Zhang. Heterogeneous Deep Graph Infomax. AAAI 2020

3. Xingyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King. Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. WWW2020

4. Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim. Graph Transformer Networks. NIPS 2019

5. Yuxin Xiao, Zecheng

   Zhang, Carl Yang, and Chengxiang Zhai. Non-local Attention Learning on Large Heterogeneous Information Networks IEEE Big Data 2019.

6. Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. KDD 2019. paper

7. Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla. Heterogeneous Graph Neural Network. KDD 2019

8. Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai and Philip S. Yu Fine-grained Event Categorization with Heterogeneous Graph Convolutional. IJCAI 2019. paper

9. Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Philip S. Yu, Yanfang Ye.Heterogeneous Graph Attention Network. WWW 2019. paper

10. Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu. Deep Collective Classification in Heterogeneous Information Networks. WWW 2018. paper

11. Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song. Heterogeneous Graph Neural Networks for Malicious Account Detection. CIKM 2018. paper

12. Marinka Zitnik, Monica Agrawal, Jure Leskovec. Modeling polypharmacy side effects with graph convolutional networks ISMB 2018 paper

Recommendation

1. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper
2. Federico Monti, Michael M. Bronstein, Xavier Bresson. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. NIPS 2017. paper
3. Rianne van den Berg, Thomas N. Kipf, Max Welling. Graph Convolutional Matrix Completion. 2017. paper
4. Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. IJCAI 2019. paper
5. Haoyu Wang, Defu Lian, Yong Ge. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. IJCAI 2019. paper
6. Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019. paper
7. Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper
8. Jin Shang, Mingxuan Sun. Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. AAAI 2019. paper
9. Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD 2019. paper
10. Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu. Exact-K Recommendation via Maximal Clique Optimization. KDD 2019. paper
11. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KGAT: Knowledge Graph Attention Network for Recommendation. KDD 2019. paper
12. Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo. Knowledge Graph Convolutional Networks for Recommender Systems. WWW 2019. paper
13. Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW 2019. paper
14. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation. WWW 2019. paper
15. Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates. Memory Augmented Graph Neural Networks for Sequential Recommendation. AAAI 2020. paper
16. Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. AAAI 2020. paper
17. Muhan Zhang, Yixin Chen. Inductive Matrix Completion Based on Graph Neural Networks. ICLR 2020. paper