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Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN).
Self-Organization in a Perceptual Network
Ralph Linsker. 1988 · 1988
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In AISTATS . 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. 2011 · 2011
Earlier work this paper cites.
Mining heterogeneous information networks: a structural analysis approach
Yizhou Sun and Jiawei Han. 2012 · 2012
Earlier work this paper cites.
Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In ICLR
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N. Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
The Comparative Toxicogenomics Database: update 2017
Allan Peter Davis, Cynthia J. Grondin, Robin J. Johnson, Daniela Sciaky, Benjamin L. King, Roy McMorran, Jolene Wiegers, Thomas C. Wiegers, and Carolyn J. Mattingly. 2017 · 2017
Earlier work this paper cites.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks. In SIGKDD . 135–144
Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs. In NeurIPS . 1024–1034
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Gotcha - Sly Malware!: Scorpion A Metagraph2vec Based Malware Detection System. In SIGKDD . 253–262
Yujie Fan, Shifu Hou, Yiming Zhang, Yanfang Ye, and Melih Abdulhayoglu. 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
GraphGAN: Graph Representation Learning With Generative Adversarial Nets. In AAAI . 2508–2515
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018 · 2018
Cited alongside, same era.
mixup: Beyond Empirical Risk Minimization. In ICLR
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
Cited alongside, same era.
Learning Representations by Maximizing Mutual Information Across Views. In NeurIPS . 15509–15519
Philip Bachman, R. Devon Hjelm, and William Buchwalter. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. In SIGKDD . 2478–2486
Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, and Yongliang Li. 2019 · 2019
Cited alongside, same era.
Momentum Contrast for Unsupervised Visual Representation Learning. In CVPR . 9726–9735
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020 · 2020
Later among the works it cites.
Hard Negative Mixing for Contrastive Learning. In NeurIPS
Yannis Kalantidis, Mert Bülent Sariyildiz, Noé Pion, Philippe Weinzaepfel, and Diane Larlus. 2020 · 2020
Later among the works it cites.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
Later among the works it cites.
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
Xiang Li, Danhao Ding, Ben Kao, Yizhou Sun, and Nikos Mamoulis. 2020 · 2020
Later among the works it cites.
Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, and Jie Tang. 2020 · 2020
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Adversarial Learning on Heterogeneous Information Networks. In SIGKDD . 120–129
Binbin Hu, Yuan Fang, and Chuan Shi. 2019 · 2019
Cited alongside, same era.
Heterogeneous Information Network Embedding for Recommendation
Chuan Shi, Binbin Hu, Wayne Xin Zhao, and Philip S. Yu. 2019 · 2019
Cited alongside, same era.
Deep Graph Infomax. In ICLR
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. 2019 · 2019
Cited alongside, same era.
Graph Transformer Networks. In NeurIPS . 11960–11970
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. 2019 · 2019
Cited alongside, same era.
Heterogeneous Graph Neural Network. In SIGKDD . 793–803
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla. 2019 · 2019
Cited alongside, same era.
A Simple Framework for Contrastive Learning of Visual Representations. In ICML . 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020 · 2020
Cited alongside, same era.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In WWW . 2331–2341
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Unsupervised Attributed Multiplex Network Embedding. In AAAI . 5371–5378
Chanyoung Park, Donghyun Kim, Jiawei Han, and Hwanjo Yu. 2020 · 2020
Later among the works it cites.
Graph Representation Learning via Graphical Mutual Information Maximization. In WWW . 259–270
Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. 2020 · 2020
Later among the works it cites.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. In KDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Later among the works it cites.
Contrastive Multiview Coding. In ECCV . 776–794
Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020 · 2020
Later among the works it cites.
Network Schema Preserving Heterogeneous Information Network Embedding. In IJCAI . 1366–1372
Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, and Yanfang Ye. 2020 · 2020
Later among the works it cites.
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021 · 2021
Closest in time.
Heterogeneous Graph Attention Network. In WWW . 2022–2032
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019 · 2032
Closest in time.