Fetching the paper…
Reading the bibliography…
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes.
Lecture notes on cryptography
Goldwasser, S. and Bellare, M · 1996
Earlier work this paper cites.
The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 2000
Earlier work this paper cites.
On choosing and bounding probability metrics
Gibbs, A. L. and Su, F. E · 2002
Earlier work this paper cites.
Visualizing data using t-sne, 2008
van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
The exponential complexity of satisfiability problems
Calabro, C · 2009
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Distributionally robust optimization and its tractable approximations
Goh, J. and Sim, M · 2010
Earlier work this paper cites.
A semantic matching energy function for learning with multi-relational data
Bordes, A., Glorot, X., Weston, J., and Bengio, Y · 2013
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Maas, A. L · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
Earlier work this paper cites.
Mlaas: Machine learning as a service
Ribeiro, M., Grolinger, K., and Capretz, M. A · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
Earlier work this paper cites.
Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
Later among the works it cites.
Adversarial multiple source domain adaptation
Zhao, H., Zhang, S., Wu, G., Moura, J. M., Costeira, J. P., and Gordon, G. J · 2018
Later among the works it cites.
Adversarial attacks on node embeddings via graph poisoning
Bojchevski, A. and Günnemann, S · 2019
Later among the works it cites.
The general black-box attack method for graph neural networks
Chang, H., Rong, Y., Xu, T., Huang, W., Zhang, H., Cui, P., Zhu, W., and Huang, J · 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…
Berg, R. v. d., Kipf, T. N., and Welling, M · 2017
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. L., Ying, R., and Leskovec, J · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Learning entity type embeddings for knowledge graph completion
Moon, C., Jones, P., and Samatova, N. F · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
Du, S. S., Hou, K., Salakhutdinov, R. R., Poczos, B., Wang, R., and Xu, K · 2019
Later among the works it cites.
Just jump: Dynamic neighborhood aggregation in graph neural networks
Fey, M · 2019
Later among the works it cites.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
Later among the works it cites.
Attacking graph convolutional networks via rewiring
Ma, Y., Wang, S., Wu, L., and Tang, J · 2019
Later among the works it cites.
Solving a class of non-convex min-max games using iterative first order methods
Nouiehed, M., Sanjabi, M., Huang, T., Lee, J. D., and Razaviyayn, M · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Composition-based multi-relational graph convolutional networks
Vashishth, S., Sanyal, S., Nitin, V., and Talukdar, P · 2019
Later among the works it cites.
Certifiable robustness and robust training for graph convolutional networks
Zügner, D. and Günnemann, S · 2019
Later among the works it cites.
Graphnorm: A principled approach to accelerating graph neural network training
Cai, T., Luo, S., Xu, K., He, D., Liu, T.-y., and Wang, L · 2020
Closest in time.
What can neural networks reason about?
Xu, K., Li, J., Zhang, M., Du, S. S., ichi Kawarabayashi, K., and Jegelka, S · 2020
Closest in time.
How neural networks extrapolate: From feedforward to graph neural networks
Xu, K., Zhang, M., Li, J., Du, S. S., Kawarabayashi, K.-I., and Jegelka, S · 2021
Closest in time.