Label Contrastive Coding based Graph Neural Network for Graph Classification

Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., Borgwardt, K.: Efficient graphlet kernels for large graph comparison. In: Artificial Intelligence and Statistics. pp. 488–495 (2009)

2009

Earlier work this paper cites.

Vishwanathan, S., Schraudolph, N.N., Kondor, R., Borgwardt, K.M.: Graph kernels. The Journal of Machine Learning Research 11

2010

Earlier work this paper cites.

Shervashidze, N., Schweitzer, P., Van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12

2011

Earlier work this paper cites.

Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. In: International Conference on Learning Representaion (2015)

2015

Earlier work this paper cites.

Yanardag, P., Vishwanathan, S.: Deep graph kernels. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1365–1374 (2015)

2015

Earlier work this paper cites.

Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Advances in neural information processing systems. pp. 1993–2001 (2016)

2016

Earlier work this paper cites.

Kondor, R., Pan, H.: The multiscale laplacian graph kernel. In: Advances in Neural Information Processing Systems. pp. 2990–2998 (2016)

2016

Earlier work this paper cites.

Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML. vol. 2, p. 7 (2016)

2016

Earlier work this paper cites.

Fout, A., Byrd, J., Shariat, B., Ben-Hur, A.: Protein interface prediction using graph convolutional networks. In: Advances in neural information processing systems. pp. 6530–6539 (2017)

2017

Earlier work this paper cites.

Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in neural information processing systems. pp. 1024–1034 (2017)

2017

Earlier work this paper cites.

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representaion (2017)

2017

Earlier work this paper cites.

Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)

Original

2017

Earlier work this paper cites.

Adhikari, B., Zhang, Y., Ramakrishnan, N., Prakash, B.A.: Sub2vec: Feature learning for subgraphs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 170–182. Springer (2018)

2018

Cited alongside, same era.

Elsayed, G., Krishnan, D., Mobahi, H., Regan, K., Bengio, S.: Large margin deep networks for classification. In: Advances in neural information processing systems. pp. 842–852 (2018)

2018

Cited alongside, same era.

Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)

Original

2018

Cited alongside, same era.

Ivanov, S., Burnaev, E.: Anonymous walk embeddings. In: International Conference on Machine Learning. pp. 2186–2195 (2018)

2018

Cited alongside, same era.

Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., Leskovec, J.: Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019)

Original

2019

Later among the works it cites.

Meng, L., Ren, Y., Zhang, J., Ye, F., Philip, S.Y.: Deep heterogeneous social network alignment. In: 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI). pp. 43–52. IEEE (2019)

2019

Later among the works it cites.

Ren, Y., Liu, B., Huang, C., Dai, P., Bo, L., Zhang, J.: Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538 (2019)

Original

2019

Later among the works it cites.

Ren, Y., Zhu, H., Zhang, J., Dai, P., Bo, L.: Ensemfdet: An ensemble approach to fraud detection based on bipartite graph. arXiv preprint arXiv:1912.11113 (2019)

Original

2019

Later among the works it cites.