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Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks.
K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel, “Protein function prediction via graph kernels,” Bioinformatics , vol. 21, no. suppl_1, pp. i47–i56, 2005
2005
Earlier work this paper cites.
K. M. Borgwardt and H.-P. Kriegel, “Shortest-path kernels on graphs,” in ICDM , 2005, pp. 8–pp
2005
Earlier work this paper cites.
N. Shervashidze, S. Vishwanathan, T. Petri, K. Mehlhorn, and K. Borgwardt, “Efficient graphlet kernels for large graph comparison,” in AISTATS , 2009, pp. 488–495
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” TKDE , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in AISTATS , 2010, pp. 297–304
2010
Earlier work this paper cites.
L. Backstrom and J. Leskovec, “Supervised random walks: predicting and recommending links in social networks,” in WSDM , 2011, pp. 635–644
2011
Earlier work this paper cites.
N. Shervashidze, P. Schweitzer, E. J. Van Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels.” JMLR , vol. 12, no. 9, 2011
2011
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” TPAMI , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
P. Yanardag and S. Vishwanathan, “Deep graph kernels,” in KDD , 2015, pp. 1365–1374
2015
Earlier work this paper cites.
R. Kondor and H. Pan, “The multiscale laplacian graph kernel,” in NeurIPS , 2016, pp. 2990–2998
2016
Earlier work this paper cites.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization with stochastic transformations and perturbations for deep semi-supervised learning,” in NeurIPS , 2016, pp. 1163–1171
2016
Earlier work this paper cites.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-vae: Learning basic visual concepts with a constrained variational framework,” in ICLR , 2016
2016
Earlier work this paper cites.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in NeurIPS , 2016, pp. 2172–2180
2016
Earlier work this paper cites.
S. Laine and T. Aila, “Temporal ensembling for semi-supervised learning,” in ICLR , 2017
2017
Earlier work this paper cites.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in NeurIPS , 2017, pp. 1195–1204
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NeurIPS , 2017, pp. 1025–1035
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Earlier work this paper cites.
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” in NeurIPS , 2018, pp. 4805–4815
2018
Earlier work this paper cites.
Y. C. Ng, N. Colombo, and R. Silva, “Bayesian semi-supervised learning with graph gaussian processes,” in NeurIPS , 2018, pp. 1690–1701
2018
Cited alongside, same era.
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm, “Mutual information neural estimation,” in ICML , 2018, pp. 531–540
2018
Cited alongside, same era.
2018
Cited alongside, same era.
B. Adhikari, Y. Zhang, N. Ramakrishnan, and B. A. Prakash, “Sub2vec: Feature learning for subgraphs,” in PAKDD , 2018, pp. 170–182
2018
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in ICLR , 2018
2018
P. Cheng, W. Hao, S. Dai, J. Liu, Z. Gan, and L. Carin, “Club: A contrastive log-ratio upper bound of mutual information,” in ICML , 2020, pp. 1779–1788
2020
Later among the works it cites.
X. Liang, D. Li, and A. Madden, “Attributed network embedding based on mutual information estimation,” in CIKM , 2020, pp. 835–844
2020
Later among the works it cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in ICML , 2020, pp. 1597–1607
2020
Later among the works it cites.
K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in ICML , 2020, pp. 4116–4126
2020
Later among the works it cites.
Y. You, T. Chen, Y. Shen, and Z. Wang, “Graph contrastive learning automated,” in ICML , 2021, pp. 12 121–12 132
2021
Later among the works it cites.
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Cited alongside, same era.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in ICLR , 2018
2018
Cited alongside, same era.
H. Linmei, T. Yang, C. Shi, H. Ji, and X. Li, “Heterogeneous graph attention networks for semi-supervised short text classification,” in EMNLP , 2019
2019
Cited alongside, same era.
J. Ma, P. Cui, K. Kuang, X. Wang, and W. Zhu, “Disentangled graph convolutional networks,” in ICML , 2019, pp. 4212–4221
2019
Cited alongside, same era.
J. Li, Y. Rong, H. Cheng, H. Meng, W. Huang, and J. Huang, “Semi-supervised graph classification: A hierarchical graph perspective,” in WWW , 2019, pp. 972–982
2019
Cited alongside, same era.
P. Velickovic, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in ICLR , 2019
2019
Cited alongside, same era.
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe, “Weisfeiler and leman go neural: Higher-order graph neural networks,” in AAAI , 2019, pp. 4602–4609
2019
Cited alongside, same era.
B. Poole, S. Ozair, A. Van Den Oord, A. Alemi, and G. Tucker, “On variational bounds of mutual information,” in ICML , 2019, pp. 5171–5180
2019
Cited alongside, same era.
H. Li, X. Wang, Z. Zhang, Z. Yuan, H. Li, and W. Zhu, “Disentangled contrastive learning on graphs,” in NeurIPS , 2021, pp. 21 872–21 884
2021
Later among the works it cites.
B. Zhang, Y. Wang, W. Hou, H. Wu, J. Wang, M. Okumura, and T. Shinozaki, “Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling,” in NeurIPS , 2021, pp. 18 408–18 419
2021
Later among the works it cites.
Y. Wang, Y. Song, S. Li, C. Cheng, W. Ju, M. Zhang, and S. Wang, “Disencite: Graph-based disentangled representation learning for context-specific citation generation,” in AAAI , 2022, pp. 11 449–11 458
2022
Later among the works it cites.
W. Ju, X. Luo, Z. Ma, J. Yang, M. Deng, and M. Zhang, “Ghnn: Graph harmonic neural networks for semi-supervised graph-level classification,” Neural Networks , vol. 151, pp. 70–79, 2022
2022
Later among the works it cites.
W. Ju, J. Yang, M. Qu, W. Song, J. Shen, and M. Zhang, “Kgnn: Harnessing kernel-based networks for semi-supervised graph classification,” in WSDM , 2022, pp. 421–429
2022
Later among the works it cites.
X. Luo, W. Ju, M. Qu, C. Chen, M. Deng, X.-S. Hua, and M. Zhang, “Dualgraph: Improving semi-supervised graph classification via dual contrastive learning,” in ICDE , 2022, pp. 699–712
2022
Later among the works it cites.
H. Yue, C. Zhang, C. Zhang, and H. Liu, “Label-invariant augmentation for semi-supervised graph classification,” in NeurIPS , 2022, pp. 29 350–29 361
2022
Later among the works it cites.
W. Ju, X. Luo, M. Qu, Y. Wang, C. Chen, M. Deng, X.-S. Hua, and M. Zhang, “Tgnn: A joint semi-supervised framework for graph-level classification,” in IJCAI , 2022, pp. 2122–2128
2022
Later among the works it cites.
Y. Wang, Y. Qin, F. Sun, B. Zhang, X. Hou, K. Hu, J. Cheng, J. Lei, and M. Zhang, “Disenctr: Dynamic graph-based disentangled representation for click-through rate prediction,” in SIGIR , 2022, pp. 2314–2318
2022
Later among the works it cites.
S. Li, X. Wang, A. Zhang, Y. Wu, X. He, and T.-S. Chua, “Let invariant rationale discovery inspire graph contrastive learning,” in ICML , 2022, pp. 13 052–13 065
2022
Later among the works it cites.
Y. Mo, Y. Lei, J. Shen, X. Shi, H. T. Shen, and X. Zhu, “Disentangled multiplex graph representation learning,” in ICML , 2023, pp. 24 983–25 005
2023
Later among the works it cites.
X. Luo, Y. Zhao, Y. Qin, W. Ju, and M. Zhang, “Towards semi-supervised universal graph classification,” TKDE , 2023
2023
Later among the works it cites.
Y. Qin, Y. Wang, F. Sun, W. Ju, X. Hou, Z. Wang, J. Cheng, J. Lei, and M. Zhang, “Disenpoi: Disentangling sequential and geographical influence for point-of-interest recommendation,” in WSDM , 2023, pp. 508–516
2023
Later among the works it cites.
W. Ju, Z. Mao, Z. Qiao, Y. Qin, S. Yi, Z. Xiao, X. Luo, Y. Fu, and M. Zhang, “Focus on informative graphs! semi-supervised active learning for graph-level classification,” Pattern Recognition , p. 110567, 2024
2024
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