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Graph Based SSL

2021

  • Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels. [pdf]

    • Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong. NeurIPS 2021

  • Topology-Imbalance Learning for Semi-Supervised Node Classification. [pdf] [code]

    • Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, Xu Sun. NeurIPS 2021

  • Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution. [pdf]

    • Huimin Huang, Lanfen Lin, Yue Zhang, Yingying Xu, Jing Zheng, XiongWei Mao, Xiaohan Qian, Zhiyi Peng, Jianying Zhou, Yen-Wei Chen, Ruofeng Tong. ICML 2021

  • Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization. [pdf]

    • Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath. ICML 2021

  • Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning. [pdf]

    • Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J. Lim, Byoung-Tak Zhang ICML 2021

  • Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and OOD Generalization. [pdf]

    • Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath ICML 2021

  • Class-Attentive Diffusion Network for Semi-Supervised Classification. [pdf] [code]

    • Jongin Lim, Daeho Um, Hyung Jin Chang, Dae Ung Jo, Jin Young Choi AAAI 2021

2020

  • Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering. [pdf] [code]

    • Meng Liu, David F. Gleich. NeurIPS 2020

  • Deep Graph Pose: a semi-supervised deep graphicalmodel for improved animal pose tracking. [pdf]

    • Anqi Wu, E. Kelly Buchanan et al. NeurIPS 2020

  • Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates. [pdf]

    • Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev. ICML 2020

  • Density-Aware Graph for Deep Semi-Supervised Visual Recognition. [pdf]

    • Suichan Li, Bin Liu, Dongdong Chen, Qi Chu, Lu Yuan, Nenghai Yu. CVPR 2020

  • Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data. [pdf]

    • Wanyu Lin, Zhaolin Gao, Baochun Li. CVPR 2020

  • InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. [pdf]

    • Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu. ICLR 2020

  • Graph Inference Learning for Semi-supervised Classification. [pdf]

    • Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu. ICLR 2020

2019

  • Improved Semi-Supervised Learning with Multiple Graphs. [pdf]

    • Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi, Partha Talukdar. AISTATS 2019

  • Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. [pdf] [code]

    • Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar. AISTATS 2019

  • Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. [pdf] [code]

    • Pedro Mercado, Francesco Tudisco, Matthias Hein. NeurIPS 2019

  • A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. [pdf]

    • Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh. NeurIPS 2019

  • Graph Agreement Models for Semi-Supervised Learning. [pdf] [code]

    • Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi, Andrew Tomkins. NeurIPS 2019

  • Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets. [pdf] [code]

    • Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, Bin Luo. NeurIPS 2019

  • A Flexible Generative Framework for Graph-based Semi-supervised Learning. [pdf] [code]

    • Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei. NeurIPS 2019

  • Semi-Supervised Learning With Graph Learning-Convolutional Networks. [pdf]

    • Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, Bin Luo. CVPR 2019

  • Label Efficient Semi-Supervised Learning via Graph Filtering. [pdf]

    • Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan. CVPR 2019

  • Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks. [pdf]

    • Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, Weixiong Zhang. AAAI 2019

  • Matrix Completion for Graph-Based Deep Semi-Supervised Learning. [pdf]

    • Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi. AAAI 2019

  • Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification. [pdf]

    • Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Ustebay. AAAI 2019

2018

  • Semi-Supervised Learning via Compact Latent Space Clustering. [pdf]

    • Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori. ICML 2018

  • Bayesian Semi-supervised Learning with Graph Gaussian Processes. [pdf]

    • Yin Cheng Ng, Nicolo Colombo, Ricardo Silva. NeurIPS 2018

  • Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning. [pdf]

    • Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang. CVPR 2018

  • Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. [pdf]

    • Y Qimai Li, Zhichao Han, Xiao-ming W. AAAI 2018

  • Interpretable Graph-Based Semi-Supervised Learning via Flows. [pdf]

    • Raif M. Rustamov, James T. Klosowski. AAAI 2018

2017

  • Semi-Supervised Classification with Graph Convolutional Networks. [pdf] [code]
    • Thomas N. Kipf, Max Welling. ICLR 2017

2016

  • Large-Scale Graph-Based Semi-Supervised Learning via Tree Laplacian Solver. [pdf]

  • Yan-Ming Zhang, Xu-Yao Zhang, Xiao-Tong Yuan, Cheng-Lin Liu. AAAI 2016

  • Revisiting Semi-Supervised Learning with Graph Embeddings. [pdf] [code]

    • Zhilin Yang, William Cohen, Ruslan Salakhudinov. ICML 2016

2014

  • Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically. [pdf]

    • Yuan Fang, Kevin Chang, Hady Lauw. ICML 2014

  • A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions. [pdf]

    • Simon Jones, Ling Shao. CVPR 2014

2014

  • Semi-supervised Eigenvectors for Locally-biased Learning. [pdf]
    • Toke Hansen, Michael W. Mahoney. NeurIPS 2012

2012

  • Semi-supervised Regression via Parallel Field Regularization. [pdf]
    • Binbin Lin, Chiyuan Zhang, Xiaofei He. NeurIPS 2011

2011

  • Unsupervised and semi-supervised learning via L1-norm graph. [pdf]

    • Feiping Nie, Hua Wang, Heng Huang, Chris Ding. ICCV 2011

  • Semi-supervised Regression via Parallel Field Regularization. [pdf]

    • Binbin Lin, Chiyuan Zhang, Xiaofei He. NeurIPS 2011

2010

  • Semi-Supervised Learning with Max-Margin Graph Cuts. [pdf]

    • Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang. AISTATS 2010

  • Large Graph Construction for Scalable Semi-Supervised Learning. [pdf]

    • Wei Liu, Junfeng He, Shih-Fu Chang. ICML 2010

2009

  • Graph construction and b-matching for semi-supervised learning. [pdf]
    • Tony Jebara, Jun Wang, Shih-Fu Chang. ICML 2009

2005

  • Cluster Kernels for Semi-Supervised Learning. [pdf]
    • Olivier Chapelle, Jason Weston, Bernhard Scholkopf. NeurIPS 2005

2004

  • Regularization and Semi-supervised Learning on Large Graphs. [pdf]
    • Mikhail Belkin, Irina Matveeva, Partha Niyogi. COLT 2004