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Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot.
Attributed graph clustering: A deep attentional embedding approach
Wang, C.; Pan, S.; Hu, R.; Long, G.; Jiang, J.; and Zhang, C. 2019 · 1906
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Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
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Contrastive learning with hard negative samples
Robinson, J.; Chuang, C.-Y.; Sra, S.; and Jegelka, S. 2020 · 2010
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Deep Fusion Clustering Network
Tu, W.; Zhou, S.; Liu, X.; Guo, X.; Cai, Z.; Cheng, J.; et al. 2020 · 2012
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Mgae: Marginalized graph autoencoder for graph clustering
Wang, C.; Pan, S.; Long, G.; Zhu, X.; and Jiang, J. 2017 · 2017
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization
Hjelm, R. D.; Fedorov, A.; Lavoie-Marchildon, S.; Grewal, K.; Bachman, P.; Trischler, A.; and Bengio, Y. 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Van den Oord, A.; Li, Y.; Vinyals, O.; et al. 2018 · 2018
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Learning graph embedding with adversarial training methods
Pan, S.; Hu, R.; Fung, S.-f.; Long, G.; Jiang, J.; and Zhang, C. 2019 · 2019
Earlier work this paper cites.
Multiple kernel clustering with neighbor-kernel subspace segmentation
Zhou, S.; Liu, X.; Li, M.; Zhu, E.; Liu, L.; Zhang, C.; and Yin, J. 2019 · 2019
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Structural deep clustering network
Bo, D.; Wang, X.; Shi, C.; Zhu, M.; Lu, E.; and Cui, P. 2020 · 2020
Earlier work this paper cites.
Debiased contrastive learning
Chuang, C.-Y.; Robinson, J.; Lin, Y.-C.; Torralba, A.; and Jegelka, S. 2020 · 2020
Earlier work this paper cites.
Adaptive graph encoder for attributed graph embedding
Cui, G.; Zhou, J.; Yang, C.; and Liu, Z. 2020 · 2020
Earlier work this paper cites.
Contrastive multi-view representation learning on graphs
Hassani, K.; and Khasahmadi, A. H. 2020 · 2020
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Hard negative mixing for contrastive learning
Kalantidis, Y.; Sariyildiz, M. B.; Pion, N.; Weinzaepfel, P.; and Larlus, D. 2020 · 2020
Cited alongside, same era.
Nodeaug: Semi-supervised node classification with data augmentation
Wang, Y.; Wang, W.; Liang, Y.; Cai, Y.; Liu, J.; and Hooi, B. 2020 · 2020
Cited alongside, same era.
Distribution shift metric learning for fine-grained ship classification in SAR images
Xu, Y.; and Lang, H. 2020 · 2020
Cited alongside, same era.
Consensus one-step multi-view subspace clustering
Zhang, P.; Liu, X.; Xiong, J.; Zhou, S.; Zhao, W.; Zhu, E.; and Cai, Z. 2020 · 2020
Cited alongside, same era.
High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation
Zhou, S.; Nie, D.; Adeli, E.; Yin, J.; Lian, J.; and Shen, D. 2020 · 2020
Cited alongside, same era.
GADMSL: Graph Anomaly Detection on Attributed Networks via Multi-scale Substructure Learning
Duan, J.; Wang, S.; Liu, X.; Zhou, H.; Hu, J.; and Jin, H. 2022 · 2022
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AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
Gao, Z.; Tan, C.; Li, S.; et al. 2022 · 2022
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Attributed Graph Clustering with Dual Redundancy Reduction
Gong, L.; Zhou, S.; Liu, X.; and Tu, W. 2022 · 2022
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Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph
Li, L.; Wang, S.; Liu, X.; Zhu, E.; Shen, L.; Li, K.; and Li, K. 2022 · 2022
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Embedding Global and Local Influences for Dynamic Graphs
Meng Liu, Y. L., Jiaming Wu. 2022 · 2022
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Zhu, Y.; Xu, Y.; Yu, F.; Liu, Q.; Wu, S.; and Wang, L. 2020 · 2020
Cited alongside, same era.
CuCo: Graph Representation with Curriculum Contrastive Learning
Chu, G.; Wang, X.; Shi, C.; and Jiang, X. 2021 · 2021
Cited alongside, same era.
Automated Self-Supervised Learning for Graphs
Jin, W.; Liu, X.; Zhao, X.; Ma, Y.; Shah, N.; and Tang, J. 2021 · 2021
Cited alongside, same era.
Augmentation-Free Self-Supervised Learning on Graphs
Lee, N.; Lee, J.; and Park, C. 2021 · 2021
Cited alongside, same era.
Inductive representation learning in temporal networks via mining neighborhood and community influences
Meng Liu, Y. L. 2021 · 2021
Cited alongside, same era.
Scalable multi-view subspace clustering with unified anchors
Sun, M.; Zhang, P.; Wang, S.; Zhou, S.; Tu, W.; Liu, X.; Zhu, E.; and Wang, C. 2021 · 2021
Cited alongside, same era.
FTSO: Effective NAS via First Topology Second Operator
Wang, L.; and Chen, L. 2021 · 2021
Cited alongside, same era.
Rethinking graph auto-encoder models for attributed graph clustering
Mrabah, N.; Bouguessa, M.; Touati, M. F.; and Ksantini, R. 2022 · 2022
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Generative De Novo Protein Design with Global Context
Tan, C.; Gao, Z.; Xia, J.; and Li, S. Z. 2022 · 2022
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Continual Multi-View Clustering
Wan, X.; Liu, J.; Liang, W.; Liu, X.; Wen, Y.; and Zhu, E. 2022 · 2022
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Multi-level disentanglement graph neural network
Wu, L.; Lin, H.; Xia, J.; Tan, C.; and Li, S. Z. 2022 · 2022
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EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Xie, F.; Zhang, Z.; Li, L.; Zhou, B.; and Tan, Y. 2022 · 2022
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A Simple Graph Neural Network via Layer Sniffer
Zeng, D.; Zhou, L.; Liu, W.; Qu, H.; and Chen, W. 2022 · 2022
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Structure-enhanced heterogeneous graph contrastive learning
Zhu, Y.; Xu, Y.; Cui, H.; Yang, C.; Liu, Q.; and Wu, S. 2022 · 2022
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Flora: dual-Frequency LOss-compensated ReAl-time monocular 3D video reconstruction
Wang, L.; Gong, Y.; Wang, Q.; Zhou, K.; and Chen, L. 2023 · 2023
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Substructure Aware Graph Neural Networks
Zeng, D.; Liu, W.; Chen, W.; Zhou, L.; Zhang, M.; and Qu, H. 2023 · 2023
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