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Awesome Graph-based Semi-supervised Learning Awesome

This repository contains graph-based semi-supervised learning (GSSL) papers mentioned in our GSSL survey.

We will update this paper list to include new GSSL papers periodically.

Citation

Please cite our paper if you find it helpful.

@article{DBLP:journals/corr/abs-2102-13303,
  author    = {Zixing Song and
               Xiangli Yang and
               Zenglin Xu and
               Irwin King},
  title     = {Graph-based Semi-supervised Learning: {A} Comprehensive Review},
  journal   = {CoRR},
  volume    = {abs/2102.13303},
  year      = {2021}
}

Content

  1. Survey
  2. Graph Construction Methods
    1. Unsupervised Approaches
    2. Supervised Approaches
  3. Label Inference Methods
    1. Graph Regularization Approaches
    2. Graph Embedding Approaches
  4. Applications
    1. Computer Vision
    2. Natural Language Processing
    3. Social Networks
    4. Biomedical Science
    5. Others
  5. Theory
  6. Datasets
  7. Tutorials
  8. Books

Survey

  1. Graph-based Semi-supervised Learning: A Comprehensive Review, arXiv preprint, 2021. Song, Z., Yang, X., Xu, Z., & King, I. paper code

Graph Construction Methods

Unsupervised approaches

  1. Topics in graph construction for semi-supervised learning, technical report, 2009. P. P. Talukdar. paper

  2. Using the mutual k-nearest neighbor graphs for semi-supervised classification on natural language data, in CoNLL, 2011. P. P. Talukdar. paper

  3. Regular graph construction for semi-supervised learning, in Journal of physics, 2014. L. Berton and A. d. A. Lopes. paper

  4. Graph construction and b-matching for semi-supervised learning, in ICML, 2009. T. Jebara, J. Wang, and S. Chang. paper

  5. Label propagation through linear neighborhoods, in ICML, 2006. F. Wang and C. Zhang. paper

  6. Sparsity induced similarity measure for label propagation, in ICCV, 2009. H. Cheng, Z. Liu, and J. Yang. paper

  7. Non-negative low rank and sparse graph for semi-supervised learning, in CVPR, 2012. L. Zhuang, H. Gao, Z. Lin, Y. Ma, X. Zhang, and N. Yu. paper

  8. Manifold-based similarity adaptation for label propagation, in NIPS, 2013. M. Karasuyama and H. Mamitsuka. paper

  9. Large graph construction for scalable semi-supervised learning, in ICML, 2012. W. Liu, J. He, and S. Chang. paper

  10. Influence of graph construction on semi-supervised learning, in ECML/PKDD, 2013. C. A. R. de Sousa, S. O. Rezende, and G. E. A. P. A. Batista. paper

Supervised Approaches

  1. Supervised neighborhood graph construction for semi-supervised classification, in Pattern Recognition, 2012. M. H. Rohban and H. R. Rabiee. paper

  2. Graph construction based on labeled instances for semi-supervised learning, in ICPR, 2014. L. Berton and A. d. A. Lopes. paper

  3. Graph construction for semisupervised learning, in IJCAI, 2015. L. Berton and A. de Andrade Lopes. paper

  4. RGCLI: Robust Graph that Considers Labeled Instances for Semi-Supervised Learning, in Neurocomputing, 2017. L. Berton, T. de Paulo Faleiros, A. Valejo, J. Valverde-Rebaza, and A. de Andrade Lopes. paper

  5. Label information guided graph construction for semi-supervised learning, in TIP, 2017. L. Zhuang, Z. Zhou, S. Gao, J. Yin, Z. Lin, and Y. Ma. paper

  6. Matrix completion for graph-based deep semi-supervised learning, in AAAI, 2019. F. Taherkhani, H. Kazemi, and N. M. Nasrabadi. paper

  7. Interactive graph construction for graph-based semi-supervised learning, in TVCG, 2021. C. Chen, Z. Wang, J. Wu, X. Wang, L. Guo, Y. Li, and S. Liu. paper demo code

  8. Particle competition and cooperation in networks for semi-supervised learning, in TKDE, 2012. F. A. Breve, L. Zhao, M. G. Quiles, W. Pedrycz, and J. Liu. paper

  9. Particle competition and cooperation in networks for semi-supervised learning with concept drift, in IJCNN, 2012. F. A. Breve and L. Zhao. paper

  10. Particle competition and cooperation for semi-supervised learning with label noise, in Neurocomputing, 2015. F. A. Breve, L. Zhao, and M. G. Quiles. paper

  11. Joint sparse graph and flexible embedding for graph-based semi-supervised learning, in Neural Networks, 2019. F. Dornaika and Y. E. Traboulsi. paper

  12. Autoencoder-based graph construction for semi-supervised learning, in ECCV, 2020. M. Kang, K. Lee, Y. H. Lee, and C. Suh. paper

  13. Graphebm: Energy-based graph construction for semi-supervised learning, in ICDM, 2020. Z. Chen, H. Cao, and K. C. Chang. paper

Label Inference Methods

Graph Regularization Approaches

  1. A regularization framework for learning from graph data, in ICML, 2004. D. Zhou and B. Scholkopf. paper

  2. Learning on graph with laplacian regularization, in NeurIPS, 2007. R. K. Ando and T. Zhang. paper

  3. Properly-weighted graph laplacian for semi-supervised learning, in Applied Mathematics & Optimization, 2019. J. Calder and D. Slepcev. paper

  4. Learning from labeled and unlabeled data with label propagation, Technical Report, 2002. X. Zhu and Z. Ghahramani. paper

  5. Semi-supervised learning using gaussian fields and harmonic functions, in ICML, 2003. X. Zhu, Z. Ghahramani, and J. D. Lafferty. paper

  6. Learning with local and global consistency, in NeurIPS, 2003. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Scholkopf. paper

  7. Analysis of p-laplacian regularization in semisupervised learning, in SIMA, 2019. D. Slepcev and M. Thorpe. paper

  8. Dynamic label propagation for semi-supervised multi-class multi-label classification, in ICCV, 2013. B. Wang, Z. Tu, and J. K. Tsotsos. paper

  9. Label propagation via teaching-to-learn and learning-to-teach, in TNNLS, 2017. C. Gong, D. Tao, W. Liu, L. Liu, and J. Yang. paper

  10. A general graph-based semisupervised learning with novel class discovery, in Neural Comput. Appl, 2010. F. Nie, S. Xiang, Y. Liu, and C. Zhang. paper

  11. Graph based constrained semisupervised learning framework via label propagation over adaptive neighborhood, in TKDE, 2015. Z. Zhang, M. Zhao, and T. W. S. Chow. paper

  12. Semisupervised dimensionality reduction and classification through virtual label regression, in IEEE Trans SMC, 2011. F. Nie, D. Xu, X. Li, and S. Xiang. paper

  13. Can the virtual labels obtained by traditional LP approaches be well encoded in wlr? in TNNLS, 2016. Q. Ye, J. Yang, T. Yin, and Z. Zhang. paper

  14. Adaptive neighborhood propagation by joint l2, 1-norm regularized sparse coding for representation and classification, in ICDM, 2016. L. Jia, Z. Zhang, L. Wang, W. Jiang, and M. Zhao. paper

  15. Robust adaptive embedded label propagation with weight learning for inductive classification, in TNNLS, 2018. Z. Zhang, F. Li, L. Jia, J. Qin, L. Zhang, and S. Yan. paper

  16. Robust triple-matrix-recovery-based auto-weighted label propagation for classification, in TNNLS, 2020. H. Zhang, Z. Zhang, M. Zhao, Q. Ye, M. Zhang, and M. Wang. paper

  17. Label propagation through linear neighborhoods, in ICML, 2006. F. Wang and C. Zhang. paper

  18. Label propagation through linear neighborhoods, in TKDE, 2008. F. Wang and C. Zhang. paper

  19. Linear neighborhood propagation and its applications, in TPAMI, 2009. J. Wang, F. Wang, C. Zhang, H. C. Shen, and L. Quan. paper

  20. Learning from labeled and unlabeled data on a directed graph, in ICML, 2005. D. Zhou, J. Huang, and B. Scholkopf. paper

  21. Semi-supervised learning by mixed label propagation, in AAAI, 2007. Tong, W. and Jin, R. paper

  22. Tikhonov regularization and semi-supervised learning on large graphs, in ICASSP, 2004. M. Belkin, I. Matveeva, and P. Niyogi. paper

  23. Manifold regularization:A geometric framework for learning from labeled and unlabeled examples, in JMLR, 2006. M. Belkin, P. Niyogi, and V. Sindhwani. paper

  24. Beyond the point cloud: from transductive to semi-supervised learning, in ICML, 2005. V. Sindhwani, P. Niyogi, and M. Belkin. paper

  25. Hyperparameter and kernel learning for graph based semi-supervised classification, in NeurIPS, 2005. Kapoor, A., Ahn, H., Qi, Y. and Picard, R. paper

  26. Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning, in ICML, 2005. Zhu, X. and Lafferty, J. paper

  27. Graph based semi-supervised learning with sharper edges, in ECML, 2005. Shin, H.H., Hill, N.J. and Rätsch, G. paper

  28. Graph-based semi-supervised learning as a generative model, in IJCAI, 2007. He, J., Carbonell, J.G. and Liu, Y. paper

  29. Active model selection for graph-based semi-supervised learning, in ICASSP, 2008. Zhao, B., Wang, F., Zhang, C. and Song, Y. paper

  30. Prototype vector machine for large scale semi-supervised learning, in ICML, 2009. Zhang, K., Kwok, J.T. and Parvin, B. paper

  31. Generalized optimization framework for graph-based semi-supervised learning, in SDM, 2012. Avrachenkov, K., Mishenin, A., Gonçalves, P. and Sokol, M. paper

  32. An iterative fusion approach to graph-based semi-supervised learning from multiple views, in PAKDD, 2014. Wang, Y., Pei, J., Lin, X., Zhang, Q. and Zhang, W. paper

  33. Large-scale graph-based semi-supervised learning via tree laplacian solver, in AAAI, 2016. Y. Zhang, X. Zhang, X. Yuan, and C. Liu. paper

  34. Large graph construction for scalable semi-supervised learning, in ICML, 2012. W. Liu, J. He, and S. Chang. paper

  35. Scalable semi-supervised learning by efficient anchor graph regularization, in TKDE, 2016. M. Wang, W. Fu, S. Hao, D. Tao, and X. Wu. paper

  36. Learning on big graph: Label inference and regularization with anchor hierarchy, in TKDE, 2017. M. Wang, W. Fu, S. Hao, H. Liu, and X. Wu. paper

  37. Deformed graph laplacian for semisupervised learning, in TNNLS, 2015. C. Gong, T. Liu, D. Tao, K. Fu, E. Tu, and J. Yang. paper

  38. Learning flexible graph-based semi-supervised embedding, in IEEE transactions on cybernetics, 2015. Dornaika, F. and El Traboulsi, Y. paper

  39. Efficient label propagation, in ICML, 2014. Y. Fujiwara and G. Irie. paper

  40. Scalable graph-based semi-supervised learning through sparse bayesian model, in TKDE, 2017. Jiang, B., Chen, H., Yuan, B., & Yao, X. paper

  41. Graph-based semi-supervised learning for relational networks, in SDM, 2017. Peel, L. paper

  42. Interpretable graph-based semi-supervised learning via flows, in AAAI, 2018. Rustamov, R.M. and Klosowski, J.T. paper

  43. Lightweight label propagation for large-scale network data, in IJCAI, 2018. D. Liang and Y. Li. paper

  44. Label propagation for deep semi-supervised learning, in CVPR, 2019. A. Iscen, G. Tolias, Y. Avrithis, and O. Chum. paper code

  45. Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods, in JMLR, 2020. F. Hoffmann, B. Hosseini, Z. Ren, and A. M. Stuart. paper

  46. Uncertainty aware graph gaussian process for semi-supervised learning, in AAAI, 2020. Liu, Z.Y., Li, S.Y., Chen, S., Hu, Y. and Huang, S.J. paper

  47. Understanding the Success of Graph-based Semi-Supervised Learning using Partially Labelled Stochastic Block Model, in IJCAI, 2020. Saha, A., Sheshadri, S., Datta, S., Ganguly, N., Makhija, D. and Patel, P. paper

  48. Poisson learning: Graph based semi-supervised learning at very low label rates, in ICML, 2020. J. Calder, B. Cook, M. Thorpe, and D. Slepcev. paper code

  49. A simple graph-based semi-supervised learning approach for imbalanced classification, in Pattern Recognition, 2021. Deng, J. and Yu, J.G. paper

  50. Lightweight label propagation for large-scale network data, in TKDE, 2021. Y. Li and D. Liang. paper

  51. Rethinking graph regularization for graph neural networks, in AAAI, 2021. H. Yang, K. Ma, and J. Cheng. paper code

Graph Embedding Approaches

  1. Nonlinear dimensionality reduction by locally linear embedding, in Science, 2000. S. T. Roweis and L. K. Saul. paper

  2. Laplacian eigenmaps and spectral techniques for embedding and clustering, in NeurIPS, 2002. M. Belkin and P. Niyogi. paper

  3. Grarep: Learning graph representations with global structural information, in CIKM, 2015. S. Cao, W. Lu, and Q. Xu. paper code

  4. Asymmetric transitivity preserving graph embedding, in KDD, 2016. M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu. paper code

  5. Deepwalk: Online learning of social representations, in KDD, 2014. B. Perozzi, R. Al-Rfou, and S. Skiena. paper code

  6. Revisiting semi-supervised learning with graph embeddings, in ICML, 2016. Z. Yang, W. Cohen, and R. Salakhudinov paper code

  7. node2vec: Scalable feature learning for networks, in KDD, 2016. A. Grover and J. Leskovec. paper code

  8. Line: Large-scale information network embedding, in WWW, 2015. J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. paper code

  9. HARP: hierarchical representation learning for networks, in AAAI, 2018. H. Chen, B. Perozzi, Y. Hu, and S. Skiena. paper code

  10. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec, in WSDM, 2018. J. Qiu, Y. Dong, H. Ma, J. Li, K. Wang, and J. Tang. paper code

  11. Structural deep network embedding, in KDD, 2016. D. Wang, P. Cui, and W. Zhu. paper code

  12. Deep neural networks for learning graph representations, in AAAI, 2016. S. Cao, W. Lu, and Q. Xu. paper code

  13. Learning graph representations with recurrent neural network autoencoders, in KDD, 2018. A. Taheri, K. Gimpel, and T. Berger-Wolf. paper

  14. Deep recursive network embedding with regular equivalence, in KDD, 2018. K. Tu, P. Cui, X. Wang, P. S. Yu, and W. Zhu. paper code

  15. Variational graph auto-encoders, arXiv preprint, 2016. T. N. Kipf and M. Welling. paper code

  16. Learning graph embedding with adversarial training methods, in IEEE Transactions on Cybernetics, 2019. S. Pan, R. Hu, S.-f. Fung, G. Long, J. Jiang, and C. Zhang. paper

  17. Semi-supervised classification with graph convolutional networks, in ICLR, 2017. T. N. Kipf and M. Welling. paper code

  18. Graph convolutional networks using heat kernel for semi-supervised learning, in IJCAI, 2019. Xu, B., Shen, H., Cao, Q., Cen, K., & Cheng, X. paper

  19. Deeper insights into graph convolutional networks for semi-supervised learning, in AAAI, 2018. Li, Q., Han, Z., & Wu, X. M. paper code

  20. Dual graph convolutional networks for graph-based semi-supervised classification, in WWW, 2018. Zhuang, C. and Ma, Q. paper code

  21. Label efficient semi-supervised learning via graph filtering, in CVPR, 2019. Li, Q., Wu, X. M., Liu, H., Zhang, X., & Guan, Z. paper code

  22. Simplifying graph convolutional networks, in ICML, 2019. F. Wu, A. H. S. Jr., T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger. paper code

  23. Deep sets, in NeurIPS, 2017. M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola. paper code

  24. Janossy pooling: Learning deep permutation-invariant functions for variablesize inputs, in ICLR, 2019. R. L. Murphy, B. Srinivasan, V. A. Rao, and B. Ribeiro. paper code

  25. Graph attention networks, in ICLR, 2018. P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. paper code

  26. Inductive representation learning on large graphs, in NeurIPS, 2017. W. Hamilton, Z. Ying, and J. Leskovec. paper code

  27. Column networks for collective classification, in AAAI, 2017. T. Pham, T. Tran, D. Q. Phung, and S. Venkatesh. paper code

  28. Gated graph sequence neural networks, in ICLR, 2016. Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel. paper code

  29. Learning a SAT solver from single-bit supervision, in ICLR, 2019. D. Selsam, M. Lamm, B. Bunz, P. Liang, L. de Moura, and D. L. Dill. paper code

  30. Representation learning on graphs with jumping knowledge networks, in ICML, 2018. K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka. paper code

  31. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization, in ICLR, 2020. F. Sun, J. Hoffmann, V. Verma, and J. Tang. paper code

  32. A flexible generative framework for graph-based semi-supervised learning, in NeurIPS, 2019. J. Ma, W. Tang, J. Zhu, and Q. Mei. paper code

  33. Non parametric graph learning for bayesian graph neural networks, in UAI, 2020. S. Pal, S. Malekmohammadi, F. Regol, Y. Zhang, Y. Xu, and M. Coates. paper

  34. Graph-based semisupervised learning with non-ignorable non-response, in NeurIPS, 2019. F. Zhou, T. Li, H. Zhou, H. Zhu, and Y. Jieping. paper code

  35. Graph agreement models for semisupervised learning, in NeurIPS, 2019. O. Stretcu, K. Viswanathan, D. Movshovitz-Attias, E. Platanios, S. Ravi, and A. Tomkins. paper code

  36. Large data and zero noise limits of graph-based semi-supervised learning algorithms, in Applied and Computational Harmonic Analysis, 2020. M. M. Dunlop, D. Slepcev, A. M. Stuart, and M. Thorpe. paper

  37. A unified framework for data poisoning attack to graph-based semi-supervised learning, in NeurIPS, 2019. X. Liu, S. Si, J. Zhu, Y. Li, and C. Hsieh. paper

  38. Graph Convolution Networks with manifold regularization for semi-supervised learning, in Neural Networks, 2020. Kejani, M.T., Dornaika, F. and Talebi, H. paper

  39. Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification, in SIGIR, 2020. Xu, B., Huang, J., Hou, L., Shen, H., Gao, J. and Cheng, X. paper

  40. Confidence-based graph convolutional networks for semi-supervised learning, in AISTATS, 2020. Vashishth, S., Yadav, P., Bhandari, M. and Talukdar, P. paper

  41. Shoestring: Graph-based semi-supervised classification with severely limited labeled data, in CVPR, 2020. Lin, W., Gao, Z., & Li, B. paper code

  42. Uncertainty aware semisupervised learning on graph data, in NeurIPS, 2020. X. Zhao, F. Chen, S. Hu, and J. Cho. paper code

  43. Contrastive and generative graph convolutional networks for graph-based semi-supervised learning. in AAAI, 2021. Wan, S., Pan, S., Yang, J., & Gong, C. paper

  44. Combining label propagation and simple models out-performs graph neural networks, in ICLR, 2021. Q. Huang, H. He, A. Singh, S. Lim, and A. R. Benson. paper code

Applications

Computer Vision

  1. A graph based subspace semi-supervised learning framework for dimensionality reduction, in ECCV, 2008. Yang, W., Zhang, S. and Liang, W. paper

  2. Integrated graph-based semi-supervised multiple/single instance learning framework for image annotation, in MM, 2008. Tang, J., Li, H., Qi, G.J. and Chua, T.S. paper

  3. Video face recognition with graph-based semi-supervised learning, in ICME, 2009. Kokiopoulou, E. and Frossard, P. paper

  4. Nonnegative sparse coding for discriminative semi-supervised learning, in CVPR, 2011. He, R., Zheng, W.S., Hu, B.G. and Kong, X.W. paper

  5. A novel graph-based fisher kernel method for semi-supervised learning, in ICPR, 2014. Rozza, A., Manzo, M. and Petrosino, A. paper

  6. Probabilistic class structure regularized sparse representation graph for semi-supervised hyperspectral image classification, in Pattern Recognition, 2017. Y. Shao, N. Sang, C. Gao, and L. Ma. paper

  7. Deep supervised hashing with anchor graph, in ICCV, 2019. Y. Chen, Z. Lai, Y. Ding, K. Lin, and W. K. Wong. paper

  8. Semisupervised and active learning through Manifold Reciprocal kNN Graph for image retrieval, in Neurocomputing, 2019. D. C. G. Pedronette, Y. Weng, A. Baldassin, and C. Hou. paper

  9. Classification-aware semisupervised domain adaptation, in CVPR, 2020. G. He, X. Liu, F. Fan, and J. You. paper

Natural Language Processing

  1. Relation extraction using label propagation based semi-supervised learning, in COLING, 2006. Chen, J., Ji, D., Tan, C.L. and Niu, Z.Y. paper

  2. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization, in NAACL, 2006. Goldberg, A.B. and Zhu, X. paper

  3. Efficient graph-based semi-supervised learning of structured tagging models, in EMNLP, 2010. A. Subramanya, S. Petrov, and F. C. N. Pereira. paper

  4. Graphbased semi-supervised conditional random fields for spoken language understanding using unaligned data, in ALTA, 2014. M. Aliannejadi, M. Kiaeeha, S. Khadivi, and S. S. Ghidary. paper

  5. A general optimization framework for smoothing language models on graph structures, in SIGIR, 2008. Q. Mei, D. Zhang, and C. Zhai. paper

  6. A graph-based semi-supervised learning for question-answering, in ACL/IJNLP, 2009. Celikyilmaz, A., Thint, M. and Huang, Z. paper

  7. A graph-based semi-supervised learning for question semantic labeling, in NAACL, 2010. Celikyilmaz, A. and Hakkani-Tur, D. paper

  8. Graph-based semi-supervised learning of translation models from monolingual data, in ACL, 2014. Saluja, A., Hassan, H., Toutanova, K. and Quirk, C. paper

  9. Query-focused multi-document summarization: Combining a topic model with graph-based semi-supervised learning, in COLING, 2014. Li, Y. and Li, S. paper

  10. Heterogeneous graph attention networks for semi-supervised short text classification, in EMNLP/IJNLP, 2019. L. Hu, T. Yang, C. Shi, H. Ji, and X. Li. paper

  11. Graph-based semi-supervised learning for natural language understanding, in EMNLP, 2019. Z. Qiu, E. Cho, X. Ma, and W. M. Campbell. paper

Social Networks

  1. Semi-supervised graph-based genre classification for web pages, in EMNLP, 2014. Asheghi, N.R., Markert, K. and Sharoff, S. paper

  2. Graph based semi-supervised learning with convolution neural networks to classify crisis related tweets, in ICWSM, 2018. F. Alam, S. R. Joty, and M. Imran. paper

  3. Bridging collaborative filtering and semi-supervised learning: A neural approach for POI recommendation, in KDD, 2017. C. Yang, L. Bai, C. Zhang, Q. Yuan, and J. Han. paper code

  4. On the supermodularity of active graph-based semi-supervised learning with stieltjes matrix regularization, in ICASSP, 2018. Chen, P.Y. and Wei, D. paper

Biomedical Science

  1. Graph-based semi-supervised gene mention tagging, in Proceedings of the 15th Workshop on Biomedical Natural Language Processing, 2016. Sheikhshab, G., Starks, E., Karsan, A., Sarkar, A. and Birol, I. paper

  2. Semi-supervised prediction of human miRNA-disease association based on graph regularization framework in heterogeneous networks, in Neurocomputing, 2018. J. Luo, P. Ding, C. Liang, and X. Chen. paper

  3. Semi-supervised learning and graph cuts for consensus based medical image segmentation, in Pattern Recognition, 2017. D. Mahapatra. paper

  4. Graph-based semisupervised one class support vector machine for detecting abnormal lung sounds, in Applied Mathematics and Computation, 2020. R. Lang, R. Lu, C. Zhao, H. Qin, and G. Liu. paper

Others

  1. Graph-based semi-supervised learning for phone and segment classification, in INTERSPEECH, 2013. Liu, Y. and Kirchhoff, K. paper

  2. Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays, in IEEE Transactions on Power Electronics, 2014. Zhao, Y., Ball, R., Mosesian, J., de Palma, J.F. and Lehman, B. paper

  3. Two step graph-based semi-supervised learning for online auction fraud detection, in ECML/PKDD, 2015. Bangcharoensap, P., Kobayashi, H., Shimizu, N., Yamauchi, S. and Murata, T. paper

  4. The counterintuitive mechanism of graph-based semi-supervised learning in the big data regime, in ICASSP, 2017. Mai, X. and Couillet, R. paper

  5. Label propagation based semi-supervised learning for software defect prediction, in ASE, 2017. Zhang, Z. W., Jing, X. Y., & Wang, T. J. paper

  6. Improved graph-based semi-supervised learning for fingerprint-based indoor localization, in GLOBECOM, 2018. Wang, D., Wang, T., Zhao, F. and Zhang, X. paper

  7. Graph-based semi-Supervised & active learning for edge flows, in KDD, 2019. Jia, J., Schaub, M. T., Segarra, S., & Benson, A. R. paper code

Theory

  1. Regularization and semisupervised learning on large graphs, in COLT, 2004. M. Belkin, I. Matveeva, and P. Niyogi. paper

  2. Graph-based semi-supervised learning and spectral kernel design, in IEEE Transactions on Information Theory, 2008. Johnson, R. and Zhang, T. paper

  3. Semi-supervised learning for graph to signal mapping: A graph signal wiener filter interpretation, in ICASSP, 2014. Girault, B., Gonçalves, P., Fleury, E. and Mor, A.S. paper

  4. On consistency of graph-based semi-supervised learning, in ICDCS, 2019. Du, C., Zhao, Y. and Wang, F. paper

  5. A sampling theory perspective of graph-based semi-supervised learning, in IEEE Transactions on Information Theory, 2019. Anis, A., El Gamal, A., Avestimehr, A. S., & Ortega, A. paper

Datasets

  1. Community structure in social and biological networks, in PNAS, 2002. Girvan, M. and Newman, M.E. paper

  2. Collective classification in network data, in AI magazine, 2008. Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B. and Eliassi-Rad, T. paper

  3. Relational learning via latent social dimensions, in KDD, 2009. Tang, L. and Liu, H. paper

  4. Don't Walk, Skip! Online learning of multi-scale network embeddings, in ASONAM, 2017. Perozzi, B., Kulkarni, V., Chen, H. and Skiena, S. paper

  5. Pitfalls of graph neural network evaluation, arXiv preprint, 2018. Shchur, O., Mumme, M., Bojchevski, A. and Günnemann, S. paper

  6. A unified weakly supervised framework for community detection and semantic matching, in PAKDD, 2018. Wang, W., Liu, X., Jiao, P., Chen, X. and Jin, D. paper

  7. SNAP Datasets: Stanford large network dataset collection, webpage collection, 2014. Leskovec, J. and Krevl, A. link

Tutorials

  1. Graph-based semi-supervised learning algorithms for NLP, in ACL, 2012. Subramanya, A. and Talukdar, P. link

Books

  1. Graph-Based Semi-Supervised Learning, in Synthesis Lectures on Artificial Intelligence and Machine Learning, 2014. Subramanya, A. and Talukdar, P.P. link

  2. Semi-supervised learning, in Adaptive Computation and Machine Learning, 2009. Chapelle, O., Scholkopf, B. and Zien, A. link

  3. Introduction to semi-supervised learning, in Synthesis lectures on artificial intelligence and machine learning, 2009. Zhu, X. and Goldberg, A.B. link

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A collection of resources for graph-based semi-supervised learning (GSSL).

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