Fetching the paper…
Reading the bibliography…
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning.
Fast variational autoencoder with inverted multi-index for collaborative filtering. In Proceedings of the ACM Web Conference 2022 . 1944–1954
Jin Chen, Defu Lian, Binbin Jin, Xu Huang, Kai Zheng, and Enhong Chen. 2022a · 1954
Earlier work this paper cites.
Algorithm AS 136: A k-means clustering algorithm
John A Hartigan and Manchek A Wong. 1979 · 1979
Earlier work this paper cites.
Learning Recommenders for Implicit Feedback with Importance Resampling. In Proceedings of the ACM Web Conference 2022 . 1997–2005
Jin Chen, Defu Lian, Binbin Jin, Kai Zheng, and Enhong Chen. 2022b · 2005
Earlier work this paper cites.
A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein. 2005 · 2005
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations
Thomas N Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Learning graph embedding with adversarial training methods
Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 2019
Earlier work this paper cites.
Adversarial graph embedding for ensemble clustering. In International Joint Conferences on Artificial Intelligence Organization
Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, and Yun Fu. 2019 · 2019
Earlier work this paper cites.
Attributed graph clustering: A deep attentional embedding approach
Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 2019
Earlier work this paper cites.
Pseudo-labeling and confirmation bias in deep semi-supervised learning. In 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–8
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness. 2020 · 2020
Earlier work this paper cites.
Structural deep clustering network. In Proceedings of The Web Conference 2020 . 1400–1410
Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, and Peng Cui. 2020 · 2020
Earlier work this paper cites.
Adaptive graph encoder for attributed graph embedding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 976–985
Ganqu Cui, Jie Zhou, Cheng Yang, and Zhiyuan Liu. 2020 · 2020
Earlier work this paper cites.
Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning . PMLR, 4116–4126
Kaveh Hassani and Amir Hosein Khasahmadi. 2020 · 2020
Earlier work this paper cites.
Deep Fusion Clustering Network
Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, Jieren Cheng, et al · 2020
Earlier work this paper cites.
Nodeaug: Semi-supervised node classification with data augmentation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 207–217
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, and Bryan Hooi. 2020 · 2020
Earlier work this paper cites.
Automated self-supervised learning for graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, and Jiliang Tang. 2021 · 2021
Earlier work this paper cites.
Augmentation-Free Self-Supervised Learning on Graphs
Namkyeong Lee, Junseok Lee, and Chanyoung Park. 2021 · 2021
Earlier work this paper cites.
Inductive representation learning in temporal networks via mining neighborhood and community influences. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2202–2206
Meng Liu and Yong Liu. 2021 · 2021
Earlier work this paper cites.
Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021 · 2021
Cited alongside, same era.
Late fusion multiple kernel clustering with proxy graph refinement
Siwei Wang, Xinwang Liu, Li Liu, Sihang Zhou, and En Zhu. 2021a · 2021
Cited alongside, same era.
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
Siwei Wang, Xinwang Liu, Xinzhong Zhu, Pei Zhang, Yi Zhang, Feng Gao, and En Zhu. 2021b · 2021
Cited alongside, same era.
Mixup for node and graph classification. In Proceedings of the Web Conference 2021 . 3663–3674
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi. 2021c · 2021
Cited alongside, same era.
Graph contrastive learning automated. In International Conference on Machine Learning . PMLR, 12121–12132
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021 · 2021
Cited alongside, same era.
Interpolation-Based Contrastive Learning for Few-Label Semi-Supervised Learning
Xihong Yang, Xiaochang Hu, Sihang Zhou, Xinwang Liu, and En Zhu. 2022a · 2022
Later among the works it cites.
Mixed Graph Contrastive Network for Semi-Supervised Node Classification
Xihong Yang, Yue Liu, Sihang Zhou, Xinwang Liu, and En Zhu. 2022b · 2022
Later among the works it cites.
Contrastive Deep Graph Clustering with Learnable Augmentation
Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Xinwang Liu, and En Zhu. 2022c · 2022
Later among the works it cites.
Autogcl: Automated graph contrastive learning via learnable view generators. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 8892–8900
Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, and Xiang Zhang. 2022 · 2022
Later among the works it cites.
SAIL: Self-Augmented Graph Contrastive Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 8927–8935
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. 2021 · 2021
Cited alongside, same era.
Graph debiased contrastive learning with joint representation clustering. In Proc. IJCAI . 3434–3440
Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. 2021 · 2021
Cited alongside, same era.
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, and Enhong Chen. 2022c · 2022
Cited alongside, same era.
Attributed Graph Clustering with Dual Redundancy Reduction. In IJCAI
Lei Gong, Sihang Zhou, Xinwang Liu, and Wenxuan Tu. 2022 · 2022
Cited alongside, same era.
Reasoning over different types of knowledge graphs: Static, temporal and multi-modal
Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, and Fuchun Sun. 2022 · 2022
Cited alongside, same era.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application
Yue Liu, Jun Xia, Sihang Zhou, Siwei Wang, Xifeng Guo, Xihong Yang, Ke Liang, Wenxuan Tu, Z. Stan Li, and Xinwang Liu. 2022c · 2022
Cited alongside, same era.
Towards unsupervised deep graph structure learning. In Proceedings of the ACM Web Conference 2022 . 1392–1403
Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan. 2022d · 2022
Cited alongside, same era.
Lu Yu, Shichao Pei, Lizhong Ding, Jun Zhou, Longfei Li, Chuxu Zhang, and Xiangliang Zhang. 2022 · 2022
Later among the works it cites.
Graph clustering network with structure embedding enhanced
Shifei Ding, Benyu Wu, Xiao Xu, Lili Guo, and Ling Ding. 2023 · 2023
Closest in time.
Cooperative Retriever and Ranker in Deep Recommenders. In Proceedings of the ACM Web Conference 2023 . 1150–1161
Xu Huang, Defu Lian, Jin Chen, Liu Zheng, Xing Xie, and Enhong Chen. 2023 · 2023
Closest in time.
Message Intercommunication for Inductive Relation Reasoning
Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, and Xinwang Liu. 2023a · 2023
Closest in time.
Abslearn: a gnn-based framework for aliasing and buffer-size information retrieval
Ke Liang, Jim Tan, Dongrui Zeng, Yongzhe Huang, Xiaolei Huang, and Gang Tan. 2023b · 2023
Closest in time.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Meng Liu, Ke Liang, Bin Xiao, Sihang Zhou, Wenxuan Tu, Yue Liu, Xihong Yang, and Xinwang Liu. 2023b · 2023
Closest in time.
Simple contrastive graph clustering
Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Siwei Wang, Ke Liang, Wenxuan Tu, and Liang Li. 2023c · 2023
Closest in time.
Neighbor Contrastive Learning on Learnable Graph Augmentation
Xiao Shen, Dewang Sun, Shirui Pan, Xi Zhou, and Laurence T Yang. 2023 · 2023
Closest in time.
Cluster-guided Contrastive Graph Clustering Network. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 10834–10842
Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, and En Zhu. 2023 · 2023
Closest in time.
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
Zaixi Zhang and Qi Liu. 2023 · 2023
Closest in time.
An equivariant generative framework for molecular graph-structure Co-design
Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, and Enhong Chen. 2023a · 2023
Closest in time.
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design
Zaixi Zhang, Jiaxian Yan, Qi Liu, and Enhong Che. 2023b · 2023
Closest in time.
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021 · 2080
Closest in time.