Fetching the paper…
Reading the bibliography…
Graph-structured data is ubiquitous in practice and often processed using graph neural networks (GNNs).
G. Jeh and J. Widom, “Scaling personalized web search,” in Proceedings of the 12th international conference on World Wide Web , 2003, pp. 271–279
2003
Earlier work this paper cites.
F. Chung, “The heat kernel as the pagerank of a graph,” Proceedings of the National Academy of Sciences , vol. 104, no. 50, pp. 19 735–19 740, 2007
2007
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, pp. 93–93, 2008
2008
Earlier work this paper cites.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies . Portland, Oregon, USA: Association for Computational Linguistics, June 2011, pp. 142–150. [Online]. Available: http://www.aclweb.org/anthology/P11-1015
2011
Earlier work this paper cites.
C. Dwork, “Differential privacy. encyclopedia of cryptography and security,” 2011
2011
Earlier work this paper cites.
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, “Differentially private empirical risk minimization.” Journal of Machine Learning Research , vol. 12, no. 3, 2011
2011
Earlier work this paper cites.
C. Sudlow, J. Gallacher, N. Allen, V. Beral, P. Burton, J. Danesh, P. Downey, P. Elliott, J. Green, M. Landray et al. , “Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age,” PLoS medicine , vol. 12, no. 3, p. e1001779, 2015
2015
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE Symposium on Security and Privacy . IEEE, 2015, pp. 463–480
2015
Earlier work this paper cites.
J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel, “Image-based recommendations on styles and substitutes,” in Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval , 2015, pp. 43–52
2015
Earlier work this paper cites.
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m: The new data in multimedia research,” Communications of the ACM , vol. 59, no. 2, pp. 64–73, 2016
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security , 2016, pp. 308–318
2016
Earlier work this paper cites.
Z. Yang, W. Cohen, and R. Salakhudinov, “Revisiting semi-supervised learning with graph embeddings,” in International conference on machine learning . PMLR, 2016, pp. 40–48
2016
Earlier work this paper cites.
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining , 2018, pp. 974–983
2018
Earlier work this paper cites.
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, “Pitfalls of graph neural network evaluation,” Relational Representation Learning Workshop, NeurIPS 2018 , 2018
2018
Cited alongside, same era.
A. Ginart, M. Guan, G. Valiant, and J. Y. Zou, “Making ai forget you: Data deletion in machine learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in International conference on machine learning . PMLR, 2019, pp. 6861–6871
2019
Cited alongside, same era.
P. Li, I. Chien, and O. Milenkovic, “Optimizing generalized pagerank methods for seed-expansion community detection,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh, “Remember what you want to forget: Algorithms for machine unlearning,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Later among the works it cites.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 2021, pp. 141–159
2021
Later among the works it cites.
A. Derrow-Pinion, J. She, D. Wong, O. Lange, T. Hester, L. Perez, M. Nunkesser, S. Lee, X. Guo, B. Wiltshire et al. , “Eta prediction with graph neural networks in google maps,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 3767–3776
2021
Later among the works it cites.
X.-M. Zhang, L. Liang, L. Liu, and M.-J. Tang, “Graph neural networks and their current applications in bioinformatics,” Frontiers in Genetics , vol. 12, 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
F. Gama, A. Ribeiro, and J. Bruna, “Stability of graph scattering transforms,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
C. Guo, T. Goldstein, A. Hannun, and L. Van Der Maaten, “Certified data removal from machine learning models,” in International Conference on Machine Learning . PMLR, 2020, pp. 3832–3842
2020
Cited alongside, same era.
J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding hd maps and agent dynamics from vectorized representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 525–11 533
2020
Cited alongside, same era.
A. Golatkar, A. Achille, and S. Soatto, “Eternal sunshine of the spotless net: Selective forgetting in deep networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9304–9312
2020
Cited alongside, same era.
2020
Cited alongside, same era.
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec, “Open graph benchmark: Datasets for machine learning on graphs,” Advances in neural information processing systems , vol. 33, pp. 22 118–22 133, 2020
2020
Cited alongside, same era.
J. Ma, B. Chang, X. Zhang, and Q. Mei, “Copulagnn: Towards integrating representational and correlational roles of graphs in graph neural networks,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
E. Chien, J. Peng, P. Li, and O. Milenkovic, “Adaptive universal generalized pagerank graph neural network,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=n6jl7fLxrP
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
C. Pan, S. Chen, and A. Ortega, “Spatio-temporal graph scattering transform,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=CF-ZIuSMXRz
2021
Later among the works it cites.
2022
Closest in time.
E. Chien, W.-C. Chang, C.-J. Hsieh, H.-F. Yu, J. Zhang, O. Milenkovic, and I. S. Dhillon, “Node feature extraction by self-supervised multi-scale neighborhood prediction,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=KJggliHbs8
2022
Closest in time.