Fetching the paper…
Reading the bibliography…
In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs.
Asymptotic evaluation of certain markov process expectations for large time. iv
Donsker, M. D. and Varadhan, S. S · 1983
Earlier work this paper cites.
Self-organization in a perceptual network
Linsker, R · 1988
Earlier work this paper cites.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., and Hansch, C · 1991
Earlier work this paper cites.
The predictive toxicology challenge 2000–2001
Helma, C., King, R. D., Kramer, S., and Srinivasan, A · 2001
Earlier work this paper cites.
On graph kernels: Hardness results and efficient alternatives
Gärtner, T., Flach, P., and Wrobel, S · 2003
Earlier work this paper cites.
Marginalized kernels between labeled graphs
Kashima, H., Tsuda, K., and Inokuchi, A · 2003
Earlier work this paper cites.
Shortest-path kernels on graphs
Borgwardt, K. M. and Kriegel, H.-P · 2005
Earlier work this paper cites.
Biological network comparison using graphlet degree distribution
Pržulj, N · 2007
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K · 2009
Earlier work this paper cites.
Libsvm: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
Earlier work this paper cites.
Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
Earlier work this paper cites.
Subgraph matching kernels for attributed graphs
Kriege, N. and Mutzel, P · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H · 2013
Earlier work this paper cites.
Joint structure feature exploration and regularization for multi-task graph classification
Pan, S., Wu, J., Zhu, X., Zhang, C., and Philip, S. Y · 2015
Earlier work this paper cites.
Semi-supervised learning with ladder networks
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., and Raiko, T · 2015
Earlier work this paper cites.
Deep graph kernels
Yanardag, P. and Vishwanathan, S · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
Cited alongside, same era.
The multiscale laplacian graph kernel
Kondor, R. and Pan, H · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Cited alongside, same era.
Learning topological representation for networks via hierarchical sampling
Fu, G., Hou, C., and Yao, X · 2019
Later among the works it cites.
Gao, H. and Ji, S · 2019
Later among the works it cites.
Diffusion improves graph learning
Klicpera, J., Weißenberger, S., and Günnemann, S · 2019
Later among the works it cites.
Lee, J., Lee, I., and Kang, J · 2019
Later among the works it cites.
Actional-structural graph convolutional networks for skeleton-based action recognition
Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., and Tian, Q · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., and Jaiswal, S · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
Cited alongside, same era.
Velikovi, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Cited alongside, same era.
Sub2vec: Feature learning for subgraphs
Adhikari, B., Zhang, Y., Ramakrishnan, N., and Prakash, B. A · 2018
Cited alongside, same era.
Mine: mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, R. D · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. S · 2019
Later among the works it cites.
Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J · 2019
Later among the works it cites.
Tian, Y., Krishnan, D., and Isola, P · 2019
Later among the works it cites.
Deep graph infomax
Velickovic, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2019
Later among the works it cites.
Relational graph representation learning for open-domain question answering
Vivona, S. and Hassani, K · 2019
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Later among the works it cites.
Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Later among the works it cites.
Graph cross networks with vertex infomax pooling
Li, M., Chen, S., Zhang, Y., and Tsang, I. W · 2020
Later among the works it cites.
Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
Later among the works it cites.
Gcc: Graph contrastive coding for graph neural network pre-training
Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., and Tang, J · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Later among the works it cites.