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Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J Smola, and Zheng Zhang. 2019c · 1909
Earlier work this paper cites.
Diffusion-convolutional neural networks. In Advances in neural information processing systems . 1993–2001
James Atwood and Don Towsley. 2016 · 2001
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining . 426–434
Yehuda Koren. 2008 · 2008
Earlier work this paper cites.
Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
A survey of collaborative filtering techniques
Xiaoyuan Su and Taghi M Khoshgoftaar. 2009 · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics . 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Advances in collaborative filtering
Yehuda Koren and Robert Bell. 2015 · 2015
Earlier work this paper cites.
Collaborative filtering with graph information: Consistency and scalable methods. In Advances in neural information processing systems . 2107–2115
Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, and Inderjit S Dhillon. 2015 · 2015
Earlier work this paper cites.
Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web . 111–112
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017 · 2017
Cited alongside, same era.
Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 335–344
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs. In Advances in neural information processing systems . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Hyperbolic graph convolutional neural networks. In Advances in neural information processing systems . 4868–4879
Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec. 2019 · 2019
Later among the works it cites.
Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks.. In International Joint Conferences on Artificial Intelligence . 3223–3230
Hogun Park and Jennifer Neville. 2019 · 2019
Later among the works it cites.
Multi-Graph Convolution Collaborative Filtering. In 2019 IEEE International Conference on Data Mining (ICDM) . IEEE, 1306–1311
Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, and Xiuqiang He. 2019 · 2019
Later among the works it cites.
Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019. 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019a · 2019
Later among the works it cites.
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Nais: Neural attentive item similarity model for recommendation
Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018 · 2018
Cited alongside, same era.
Variational autoencoders for collaborative filtering. In Proceedings of the 2018 World Wide Web Conference . 689–698
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
Cited alongside, same era.
Graph capsule convolutional neural networks
Saurabh Verma and Zhi-Li Zhang. 2018 · 2018
Cited alongside, same era.
Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition . 7794–7803
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He. 2018 · 2018
Cited alongside, same era.
Capsule graph neural network. In International conference on learning representations
Zhang Xinyi and Lihui Chen. 2018 · 2018
Cited alongside, same era.
HOP-rec: high-order proximity for implicit recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems . 140–144
Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018 · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 793–803
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019 · 2019
Later among the works it cites.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Closest in time.
Heterogeneous graph transformer. In Proceedings of The Web Conference 2020 . 2704–2710
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020 · 2020
Closest in time.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, and Alexander J Smola. 2020 · 2020
Closest in time.
Neighbor Interaction Aware Graph Convolution Networks for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 1289–1298
Jianing Sun, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Xiuqiang He, Chen Ma, and Mark Coates. 2020 · 2020
Closest in time.
Disentangled Graph Collaborative Filtering. In Proceedings of the 43nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Xi’an, China, July 25-30, 2020
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020 · 2020
Closest in time.
Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, and Yongdong Zhang. 2020 · 2020
Closest in time.
Heterogeneous graph attention network. In The World Wide Web Conference . 2022–2032
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019b · 2032
Closest in time.