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Graph structured data have enabled several successful applications such as recommendation systems and traffic prediction, given the rich node features and edges information.
1908
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B. Jayaraman and D. Evans, “Evaluating differentially private machine learning in practice,” in 28th USENIX Security Symposium (USENIX Security 19) . Santa Clara, CA: USENIX Association, Aug. 2019, pp. 1895–1912. [Online]. Available: https://www.usenix.org/conference/usenixsecurity19/presentation/jayaraman
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E. Zheleva and L. Getoor, “Preserving the privacy of sensitive relationships in graph data,” in Proceedings of the First SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007) , ser. Lecture Notes in Computer Science, vol. 4890. Springer, March 2007, pp. 153–171
2007
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M. Hay, G. Miklau, D. Jensen, P. Weis, and S. Srivastava, “Anonymizing social networks,” University of Massachusetts Amherst, Tech. Rep. 07-19, March 2007
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L. Zhang and W. Zhang, “Edge anonymity in social network graphs.” in CSE (4) . IEEE Computer Society, 2009, pp. 1–8. [Online]. Available: http://dblp.uni-trier.de/db/conf/cse/cse2009-4.html#ZhangZ09
2009
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M. Hay, C. Li, G. Miklau, and D. Jensen, “Accurate estimation of the degree distribution of private networks,” in 2009 Ninth IEEE International Conference on Data Mining . IEEE, 2009, pp. 169–178
2009
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F. D. McSherry, “Privacy integrated queries: an extensible platform for privacy-preserving data analysis,” in Proceedings of the 2009 ACM SIGMOD International Conference on Management of data , 2009, pp. 19–30
2009
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A. Sala, X. Zhao, C. Wilson, H. Zheng, and B. Y. Zhao, “Sharing graphs using differentially private graph models,” in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference , 2011, pp. 81–98
2011
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A. M. Fard, K. Wang, and P. S. Yu, “Limiting link disclosure in social network analysis through subgraph-wise perturbation.” in EDBT , E. A. Rundensteiner, V. Markl, I. Manolescu, S. Amer-Yahia, F. Naumann, and I. Ari, Eds. ACM, 2012, pp. 109–119. [Online]. Available: http://dblp.uni-trier.de/db/conf/edbt/edbt2012.html#FardWY12
2012
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J. Blocki, A. Blum, A. Datta, and O. Sheffet, “The johnson-lindenstrauss transform itself preserves differential privacy,” in 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science . IEEE, 2012, pp. 410–419
2012
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S. P. Kasiviswanathan, K. Nissim, S. Raskhodnikova, and A. Smith, “Analyzing graphs with node differential privacy,” in Theory of Cryptography Conference . Springer, 2013, pp. 457–476
2013
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Y. Wang and X. Wu, “Preserving differential privacy in degree-correlation based graph generation,” vol. 6, no. 2, p. 127–145, Aug. 2013
2013
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N. Li, W. Qardaji, D. Su, Y. Wu, and W. Yang, “Membership privacy: A unifying framework for privacy definitions,” in Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 889–900. [Online]. Available: https://doi.org/10.1145/2508859.2516686
2013
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——, “Differentially private data analysis of social networks via restricted sensitivity,” in Proceedings of the 4th conference on Innovations in Theoretical Computer Science , 2013, pp. 87–96
2013
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C. Dwork and A. Roth, The Algorithmic Foundations of Differential Privacy , 2014. [Online]. Available: https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
2014
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V. Karwa, S. Raskhodnikova, A. Smith, and G. Yaroslavtsev, “Private analysis of graph structure,” ACM Trans. Database Syst. , vol. 39, no. 3, Oct. 2014. [Online]. Available: https://doi.org/10.1145/2611523
2014
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Q. Xiao, R. Chen, and K.-L. Tan, “Differentially private network data release via structural inference,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining , 2014, pp. 911–920
2014
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
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Y. Mülle, C. Clifton, and K. Böhm, “Privacy-integrated graph clustering through differential privacy,” CEUR Workshop Proceedings , vol. 1330, pp. 247–254, 01 2015
2015
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P. Flach and M. Kull, “Precision-recall-gain curves: Pr analysis done right,” in Advances in Neural Information Processing Systems , C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds., vol. 28. Curran Associates, Inc., 2015, pp. 838–846. [Online]. Available: https://proceedings.neurips.cc/paper/2015/file/33e8075e9970de0cfea955afd4644bb2-Paper.pdf
2015
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S. Brunet, S. Canard, S. Gambs, and B. Olivier, “Novel differentially private mechanisms for graphs.” IACR Cryptology ePrint Archive , vol. 2016, p. 745, 2016. [Online]. Available: http://dblp.uni-trier.de/db/journals/iacr/iacr2016.html#BrunetCGO16
L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 9, p. 3848–3858, Sep 2020. [Online]. Available: http://dx.doi.org/10.1109/TITS.2019.2935152
2019
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J. Jiang, J. Chen, T. Gu, K. R. Choo, C. Liu, M. Yu, W. Huang, and P. Mohapatra, “Anomaly detection with graph convolutional networks for insider threat and fraud detection,” in MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) , 2019, pp. 109–114
2019
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B. Rozemberczki, C. Allen, and R. Sarkar, “Multi-scale attributed node embedding,” 2019
2019
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A. Bojchevski and S. Günnemann, “Adversarial attacks on node embeddings via graph poisoning,” in International Conference on Machine Learning . PMLR, 2019, pp. 695–704
2019
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2016
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T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in Proceedings of the 5th International Conference on Learning Representations , ser. ICLR ’17, 2017. [Online]. Available: https://openreview.net/forum?id=SJU4ayYgl
2017
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W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs.” in NIPS , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 1024–1034. [Online]. Available: http://dblp.uni-trier.de/db/conf/nips/nips2017.html#HamiltonYL17
2017
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Z. Qin, T. Yu, Y. Yang, I. Khalil, X. Xiao, and K. Ren, “Generating synthetic decentralized social graphs with local differential privacy,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 425–438
2017
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G. Sood, clarifai: R Client for the Clarifai API , 2017, r package version 0.4.2
2017
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2017
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2017
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R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems.” in KDD , Y. Guo and F. Farooq, Eds. ACM, 2018, pp. 974–983. [Online]. Available: http://dblp.uni-trier.de/db/conf/kdd/kdd2018.html#YingHCEHL18
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Dong, H. Su, B. Wu, Z. Li, W. Liu, T. Zhang, and J. Zhu, “Efficient decision-based black-box adversarial attacks on face recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7714–7722
2019
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2020
Later among the works it cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , 2020
2020
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Y. Rong, W. Huang, T. Xu, and J. Huang, “Dropedge: Towards deep graph convolutional networks on node classification.” in ICLR . OpenReview.net, 2020. [Online]. Available: http://dblp.uni-trier.de/db/conf/iclr/iclr2020.html#RongHXH20
2020
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H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. K. Prasanna, “Graphsaint: Graph sampling based inductive learning method.” in ICLR . OpenReview.net, 2020. [Online]. Available: http://dblp.uni-trier.de/db/conf/iclr/iclr2020.html#ZengZSKP20
2020
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V. Duddu, A. Boutet, and V. Shejwalkar, “Quantifying privacy leakage in graph embedding,” 2020
2020
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B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans, “Revisiting membership inference under realistic assumptions,” 2020
2020
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T. Humphries, M. Rafuse, L. Tulloch, S. Oya, I. Goldberg, and F. Kerschbaum, “Differentially private learning does not bound membership inference,” 2020
2020
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“Vertex ai — google cloud,” https://cloud.google.com/vertex-ai , (Accessed on 06/22/2021)
2021
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“Parlai,” https://ai.facebook.com/tools/parlai , (Accessed on 06/22/2021)
2021
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“Infosphere virtual data pipeline — ibm,” https://www.ibm.com/products/ibm-infosphere-virtual-data-pipeline , (Accessed on 06/22/2021)
2021
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X. He, J. Jia, M. Backes, N. Z. Gong, and Y. Zhang, “Stealing links from graph neural networks,” in 30th USENIX Security Symposium (USENIX Security 21) . USENIX Association, Aug. 2021. [Online]. Available: https://www.usenix.org/conference/usenixsecurity21/presentation/he-xinlei
2021
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2021
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