zhh0998/Awesome-GNN-Recommendation

Graph Neural Network

[TOC]

Introduction

Graph neural network, as a powerful graph representation learning method, has been widely used in diverse scenarios, such as NLP, CV, and recommender systems.

As far as I can see, graph mining is highly related to recommender systems. Recommend one item to one user actually is the link prediction on the user-item graph.

This repository mainly consists of three parts:

• Graph Neural Network
• GNN based Recommendation
• GNN related Resources
  • Materials & Paper & Code
• Dataset for GNN or Recommendation

We also have an Wechat Official Account, providing some materials about GNN and Recommendation.

You're most welcome to join us with any contributions for GNN and Recommendation! Here is the template for contributors:

[ID] Authors. **Paper_Name**. Conference&Year. [Paper](Paper_Link)

A simple example for template:

1. Long, Qingqing and Jin, Yilun and Song, Guojie and Li, Yi and Lin, Wei. **Graph Structural-topic Neural Network**. KDD 2020. [paper](https://arxiv.org/abs/2006.14278)

Graph Neural Network

1. Giannis Nikolentzos and Michalis Vazirgiannis.Random Walk Graph Neural Networks. NeurIPS 2020.paper
2. Nicolas Keriven and Alberto Bietti and Samuel Vaiter. Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS 2020. paper
3. Nikolaos Karalias and Andreas Loukas. Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. NeurIPS 2020. paper
4. Xiang Zhang and Marinka Zitnik. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. NeurIPS 2020. paper
5. Zheng Ma and Junyu Xuan and Yu Guang Wang and Ming Li and Pietro Lio. Path Integral Based Convolution and Pooling for Graph Neural Networks NeurIPS 2020. paper

more

6. Daniel D. Johnson and Hugo Larochelle and Daniel Tarlow. Learning Graph Structure With A Finite-State Automaton Layer. NeurIPS 2020. paper
7. Vitaly Kurin and Saad Godil and Shimon Whiteson and Bryan Catanzaro. Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning. NeurIPS 2020. paper
8. Zhiwei Deng and Karthik Narasimhan and Olga Russakovsky. Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation NeurIPS 2020. paper
9. Long, Qingqing and Jin, Yilun and Song, Guojie and Li, Yi and Lin, Wei. Graph Structural-topic Neural Network. KDD 2020. paper
10. Zang, Chengxi and Wang, Fei. Neural Dynamics on Complex Networks KDD2020. paper
11. Ganqu Cui, Jie Zhou, Cheng Yang, Zhiyuan Liu. Adaptive Graph Encoder for Attributed Graph Embedding KDD 2020. paper
12. Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution. AAAI 2018
13. Dynamic Network Embedding by Modeling Triadic Closure Process. AAAI 2018
14. DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI 2018
15. A Generative Model for Dynamic Networks with Applications. AAAI 2019
16. Communication-optimal distributed dynamic graph clustering. AAAI 2019
17. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. AAAI 2020
18. Dynamic Network Pruning with Interpretable Layerwise Channel Selection. AAAI 2020
19. DyRep: Learning Representations over Dynamic Graphs. ICLR 2019
20. Dynamic Graph Representation Learning via Self-Attention Networks. ICLR 2019
21. The Logical Expressiveness of Graph Neural Networks. ICLR 2020
22. Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees. WWW 2018
23. Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding. IJCAI 2018
24. Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks. IJCAI 2018
25. AddGraph: Anomaly Detection in Dynamic Graph using Attention-based Temporal GCN. IJCAI 2019
26. Network Embedding and Change Modeling in Dynamic Heterogeneous Networks. SIGIR 2019
27. Learning Dynamic Node Representations with Graph Neural Networks. SIGIR 2020
28. Dynamic Link Prediction by Integrating Node Vector Evolution and Local Neighborhood Representation. SIGIR 2020
29. NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. KDD 2018
30. Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. KDD 2019
31. Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. KDD 2019
32. Laplacian Change Point Detection for Dynamic Graphs. KDD 2020
33. Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction Neural Dynamics on Complex Networks KDD 2020
34. Fast Approximate Spectral Clustering for Dynamic Networks. ICML 2018
35. Improved Dynamic Graph Learning through Fault-Tolerant Sparsification. ICML 2019
36. Efficient SimRank Tracking in Dynamic Graphs. ICDE 2018
37. On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs. ICDE 2018
38. Computing a Near-Maximum Independent Set in Dynamic Graphs. ICDE 2019
39. Finding Densest Lasting Subgraphs in Dynamic Graphs: A Stochastic Approach. ICDE 2019
40. Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks. ICDE 2019
41. Adaptive Dynamic Bipartite Graph Matching: A Reinforcement Learning Approach. ICDE 2019
42. A Fast Sketch Method for Mining User Similarities Over Fully Dynamic Graph Streams.
43. Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun. Heterogeneous Graph Transformer. WWW 2020
44. Yuxiang Ren and Bo Liu and Chao Huang and Peng Dai and Liefeng Bo and Jiawei Zhang. Heterogeneous Deep Graph Infomax. AAAI 2020
45. Xingyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King. Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. WWW2020
46. Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim. Graph Transformer Networks. NIPS 2019
47. Yuxin Xiao, Zecheng Zhang, Carl Yang, and Chengxiang Zhai. Non-local Attention Learning on Large Heterogeneous Information Networks IEEE Big Data 2019.
48. Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li. KDD 2019. paper
49. Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla. Heterogeneous Graph Neural Network. KDD 2019
50. Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai and Philip S. Yu IJCAI 2019. paper
51. Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Philip S. Yu, Yanfang Ye. WWW 2019. paper
52. Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu. WWW 2018. paper
53. Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song. CIKM 2018. paper
54. Marinka Zitnik, Monica Agrawal, Jure Leskovec. ISMB 2018
55. Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji. XGNN: Towards Model-Level Explanations of Graph Neural Networks KDD2020. paper
56. Lei Yang, Qingqiu Huang, Huaiyi Huang, Linning Xu, and Dahua LinLearn to Propagate Reliably on Noisy Affinity Graphs ECCV2020. paper
57. Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin. Streaming Graph Neural Networks SIGIR2020. paper

GNN based 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

more

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
18. Xueya Zhang, Tong Zhang, Xiaobin Hong, Zhen Cui, and Jian Yang. Graph Wasserstein Correlation Analysis for Movie Retrieval ECCV 2020. paper
19. Xiaowei Jia , Handong Zhao , Zhe Lin , Ajinkya Kale , Vipin Kumar. Personalized Image Retrieval with Sparse Graph Representation Learning KDD2020. paper
20. Tianwen Chen, Raymond Chi-Wing Wong. Handling Information Loss of Graph Neural Networks for Session-based Recommendation KDD 2020 paper
21. Jianxin Chang, Chen Gao, Xiangnan He, Yong Li, Depeng Ji. Bundle Recommendation with Graph Convolutional Networks SIGIR2020. paper
22. Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, Lun-Wei Ku. MVIN: Learning Multiview Items for Recommendation SIGIR2020. paper
23. Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation SIGIR2020 paper
24. Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He. Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach SIGIR2020. paper
25. Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang. Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation SIGIR2020 paper
26. Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection SIGIR2020 paper
27. Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation SIGIR2020 paper
28. Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu. Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View SIGIR2020 paper

GNN related Resouces

Video Class

Class for new people who are interested in GNN and Recommendation.

Link: https://www.epubit.com/courseDetails?id=PCC72369cd0eb9e7

Meterials

Zhihu Link https://zhuanlan.zhihu.com/c_1158788280744173568

Here are some meterials in my Zhihu.

QQ & Wechat

Dataset for GNN or Recommendation

ACM-1

(Source: https://github.com/zyz282994112/GraphInception/tree/master/data)

Entity	#Entity
Paper	12.5k
Author	/
Conf	/
Term (paper feature)	300
Index(paper label)	11

ACM-2

(Source: https://github.com/Jhy1993/HAN)

Entity	#Entity
Paper	3025
Author	5835
Subject	56
Term (paper feature)	1830
Research area(paper label)	3

ACM-3

Entity	#Entity
Paper	12k
Author	17k
Affiliations	1.8k
Term	1.5k
Subjects	73

MovieLens

(Containing rating and timestamp information)

(Note: We utilize the Pearson's coefficient to measure the similiarities in the KNN algorithm)

(Source : https://grouplens.org/datasets/movielens/)

Entity	#Entity
User	943
Age	8
Occupation	21
Movie	1,682
Genre	18

Relation Statistics

Relation	#Relation
User - Movie	100,000
User - User (KNN)	47,150
User - Age	943
User - Occupation	943
Movie - Movie (KNN)	82,798
Movie - Genre	2,861

Douban Movie

(Containing rating information)

Entity Statistics

Entity	#Entity
User	13,367
Movie	12,677
Group	2,753
Actor	6,311
Director	2,449
Type	38

Relation Statistics

Relation	#Relation
User - Movie	1,068,278
User - Group	570,047
User - User	4,085
Movie - Actor	33,587
Movie - Director	11,276
Movie - Type	27,668

Douban Book

(Containing rating information)

Entity Statistics

Entity	#Entity
User	13,024
Book	22,347
Group	2,936
Location	38
Author	10,805
Publisher	1,815
Year	64

Relation Statistics

Relation	#Relation
User - Book	792,062
User - Group	1,189,271
User - User	169,150
User - Location	10,592
Book - Author	21,907
Book - Publisher	21,773
Book - Year	21,192

Amazon

(Containing rating and timestamp information)

(Source : http://jmcauley.ucsd.edu/data/amazon/)

Entity Statistics

Entity	#Entity
User	6,170
Item	2,753
View	3,857
Category	22
Brand	334

Relation Statistics

Relation	#Relation
User - Item	195,791
Item - View	5,694
Item - Category	5,508
Item - Brand	2,753

LastFM

(Note: We utilize the Pearson's coefficient to measure the similiarities in the KNN algorithm)

(Source : https://grouplens.org/datasets/hetrec-2011/)

Entity Statistics

Entity	#Entity
User	1,892
Artist	17,632
Tag	11,945

Relation Statistics

Relation	#Relation
User - Artist	92834
User - User (Original)	25,434
User - User (KNN)	18,802
Artist - Artist (KNN)	153,399
Artist - Tag	184,941

Yelp

(Containing rating information)

Entity Statistics

Entity	#Entity
User	16,239
Business	14,284
Compliment	11
Category	47
City	511

Relation Statistics

Relation	#Relation
User - Business	198,397
User - User	158,590
User - Compliment	76,875
Business - City	14,267
Business - Category	40,009

Yelp-2

(Containing rating information)

Entity Statistics

Entity	#Entity
User	1,286
Business	2,614
Service	2
Star level	9
Reservation	2
Category	3

Relation Statistics

Relation	#Relation
User - Business	30,838
Bussiness - Service	2,614
Bussiness - Star level	2,614
Business - Revervation	2,614
Business - Category	2,614

DBLP-1

(Note: author_map_id.dat map the author id to the unique id)

Entity Statistics

Entity	#Entity
Author	14,475
Paper	14,376
Author_label	4
Conference	20
Type	8,920

Relation Statistics

Relation	#Relation
Author - Label	4,057
Paper - Author	41,794
Paper - Conference	14,376
Paper - Type	114,624

DBLP-2

(Source: https://github.com/Jhy1993/HAN)

Entity	#Entity
Paper	14328
Author	4057
Conf	20
Term	8789
Profile(author feature)	334
Research area(author label)	4

Aminer

(Note: author_map_id.dat map the author id to the unique id)

Entity Statistics

Entity	#Entity
Author	164,472
Paper	127,623
Papel_label	10
Conference	101
Reference	147,251

Relation Statistics

Relation	#Relation
Paper - Label	127,623
Paper - Author	355,072
Paper - Conference	127,632
Paper - Reference	392,519

IMDB

(Source: https://github.com/zyz282994112/GraphInception/tree/master/data)

链接:https://pan.baidu.com/s/1pRGfoGrOsOKs-x6o5KgHmg 密码:o0ap

Entity	#Entity
Movie	14475
Actress	/
Actor	/
Director	/
Plot(movie feature)	1000
Genre(movie label)	9

SLAP

(Source: https://github.com/zyz282994112/GraphInception/tree/master/data)

链接:https://pan.baidu.com/s/1Vv6823BaAd2wRPpQHDEWUg 密码:dt5p

Entity	#Entity
Gene	20419
Ontology(gene feature)	3000
Tissue	/
Pathway	/
Diease	/
Chemical Compound	/
Family(gene label)	15

This repository is based on https://github.com/librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding. Thanks to librahu.