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Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs.
Support Vector Machines: A Nonlinear Modelling and Control Perspective
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Variational Graph Auto-Encoders
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metapath2vec: Scalable Representation Learning for Heterogeneous Networks. In SIGKDD . 135–144
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Semi-Supervised Classification with Graph Convolutional Networks. In the Fifth International Conference on Learning Representations (ICLR 2017)
Thomas N. Kipf and Max Welling. 2017 · 2017
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A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and Philip S. Yu. 2017 · 2017
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SHINE+: A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks
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Graph Attention Networks. In ICLR
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. In SIGKDD . 2478–2486
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Adversarial Learning on Heterogeneous Information Networks. In SIGKDD . 120–129
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Diffusion Improves Graph Learning. In NeurIPS . 13333–13345
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann. 2019 · 2019
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Relation Structure-Aware Heterogeneous Information Network Embedding. In AAAI . 4456–4463
Yuanfu Lu, Chuan Shi, Linmei Hu, and Zhiyuan Liu. 2019 · 2019
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Deep Graph Infomax. In ICLR
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. 2019 · 2019
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Graph Transformer Networks. In NeurIPS . 11960–11970
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. 2019 · 2019
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Heterogeneous Graph Neural Network. In SIGKDD . 793–803
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla. 2019 · 2019
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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
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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
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Heterogeneous Graph Transformer. In WWW . 2704–2710
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020 · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. In IJCAIl . 1477–1483
Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan. 2021 · 2021
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Graph Self-Supervised Learning: A Survey
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Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks. In SIGKDD . 1150–1160
Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. 2021 · 2021
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Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning. In SIGKDD . 1726–1736
Xiao Wang, Nian Liu, Hui Han, and Chuan Shi. 2021 · 2021
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Graph neural networks for natural language processing: A survey
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Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
Cited alongside, same era.
Deep multiplex graph infomax: Attentive multiplex network embedding using global information
Chanyoung Park, Jiawei Han, and Hwanjo Yu. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. In SIGKDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Cited alongside, same era.
Graph Contrastive Learning with Adaptive Augmentation
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu and L. Wang. 2020 · 2020
Cited alongside, same era.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han. 2020 · 2020
Cited alongside, same era.
Graph Contrastive Learning with Augmentations. In NeurIPS
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
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Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long. 2021 · 2021
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Self-Supervised Learning of Graph Neural Networks: A Unified Review
Yaochen Xie, Zhao Xu, Zhengyang Wang, and Shuiwang Ji. 2021 · 2021
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Heterogeneous Graph Structure Learning for Graph Neural Networks. AAAI, 4697–4705
Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye. 2021 · 2021
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Deep Graph Structure Learning for Robust Representations: A Survey
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang. 2021b · 2021
Later among the works it cites.
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, and Shu Wu. 2021a · 2021
Later among the works it cites.
Self-supervised Short-text Modeling through Auxiliary Context Generation
Nurendra Choudhary, Charu C. Aggarwal, Karthik Subbian, and Chandan K. Reddy. 2022 · 2022
Closest in time.
Large-Scale Representation Learning on Graphs via Bootstrapping. In ICLR
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, and Michal Valko. 2022 · 2022
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Graph Neural Networks: Foundations, Frontiers, and Applications
Lingfei Wu, Peng Cui, Jian Pei, and Liang Zhao. 2022 · 2022
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Graph Neural Networks: Taxonomy, Advances, and Trends
Yu Zhou, Haixia Zheng, Xin Huang, Shufeng Hao, Dengao Li, and Jumin Zhao. 2022 · 2022
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A Survey on Graph Structure Learning: Progress and Opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu. 2022 · 2022
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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.