{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:28:35Z","timestamp":1778347715665,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Crystal Center","award":["E-251-00-0105-01"],"award-info":[{"award-number":["E-251-00-0105-01"]}]},{"name":"National University of Singapore ODPRT","award":["R252-000-A81-133"],"award-info":[{"award-number":["R252-000-A81-133"]}]},{"name":"National Research Foundation Singapore","award":["NRF-MRFFAI1-2019-0004"],"award-info":[{"award-number":["NRF-MRFFAI1-2019-0004"]}]},{"name":"Ministry of Education Singapore","award":["A-0008530-00-00"],"award-info":[{"award-number":["A-0008530-00-00"]}]},{"name":"Ministry of Education Singapore","award":["T2EP20121-0011"],"award-info":[{"award-number":["T2EP20121-0011"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,7]]},"DOI":"10.1145\/3548606.3560705","type":"proceedings-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T11:41:28Z","timestamp":1667821288000},"page":"1813-1827","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["LPGNet"],"prefix":"10.1145","author":[{"given":"Aashish","family":"Kolluri","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teodora","family":"Baluta","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan","family":"Hooi","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prateek","family":"Saxena","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2422436.2422449"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning.","author":"Bojchevski Aleksandar","year":"2019","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann . 2019 . Adversarial attacks on node embeddings via graph poisoning . In International Conference on Machine Learning. Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Adversarial attacks on node embeddings via graph poisoning. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_6_1","volume-title":"Differentially private empirical risk minimization. Journal of Machine Learning Research","author":"Chaudhuri Kamalika","year":"2011","unstructured":"Kamalika Chaudhuri , Claire Monteleoni , and Anand D Sarwate . 2011. Differentially private empirical risk minimization. Journal of Machine Learning Research ( 2011 ). Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011. Differentially private empirical risk minimization. Journal of Machine Learning Research (2011)."},{"key":"e_1_3_2_1_7_1","volume-title":"Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247","author":"Chen Jie","year":"2018","unstructured":"Jie Chen , Tengfei Ma , and Cao Xiao . 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 ( 2018 ). Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465304"},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning.","author":"Choquette-Choo Christopher A","year":"2021","unstructured":"Christopher A Choquette-Choo , Florian Tramer , Nicholas Carlini , and Nicolas Papernot . 2021 . Label-only membership inference attacks . In International Conference on Machine Learning. Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021. Label-only membership inference attacks. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on machine learning.","author":"Dai Hanjun","year":"2018","unstructured":"Hanjun Dai , Hui Li , Tian Tian , Xin Huang , Lin Wang , Jun Zhu , and Le Song . 2018 . Adversarial attack on graph structured data . In International Conference on machine learning. Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018. Adversarial attack on graph structured data. In International Conference on machine learning."},{"key":"e_1_3_2_1_11_1","volume-title":"Gaurav Aggarwal, and Prateek Jain.","author":"Daigavane Ameya","year":"2021","unstructured":"Ameya Daigavane , Gagan Madan , Aditya Sinha , Abhradeep Guha Thakurta , Gaurav Aggarwal, and Prateek Jain. 2021 . Node-Level Differentially Private Graph Neural Networks . arXiv preprint arXiv:2111.15521 (2021). Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, and Prateek Jain. 2021. Node-Level Differentially Private Graph Neural Networks. arXiv preprint arXiv:2111.15521 (2021)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2926745"},{"key":"e_1_3_2_1_13_1","volume-title":"Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems ( 2016 ). Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems (2016)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271736"},{"key":"e_1_3_2_1_15_1","volume-title":"Quantifying Privacy Leakage in Graph Embedding. arXiv preprint arXiv:2010.00906","author":"Duddu Vasisht","year":"2020","unstructured":"Vasisht Duddu , Antoine Boutet , and Virat Shejwalkar . 2020. Quantifying Privacy Leakage in Graph Embedding. arXiv preprint arXiv:2010.00906 ( 2020 ). Vasisht Duddu, Antoine Boutet, and Virat Shejwalkar. 2020. Quantifying Privacy Leakage in Graph Embedding. arXiv preprint arXiv:2010.00906 (2020)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Aaron Roth etal 2014. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science (2014).  Cynthia Dwork Aaron Roth et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science (2014).","DOI":"10.1561\/9781601988195"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.11"},{"key":"e_1_3_2_1_22_1","volume-title":"USENIX Security Symposium.","author":"He Xinlei","year":"2021","unstructured":"Xinlei He , Jinyuan Jia , Michael Backes , Neil Zhenqiang Gong , and Yang Zhang . 2021 . Stealing links from graph neural networks . In USENIX Security Symposium. Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. 2021. Stealing links from graph neural networks. In USENIX Security Symposium."},{"key":"e_1_3_2_1_23_1","volume-title":"Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163","author":"Henaff Mikael","year":"2015","unstructured":"Mikael Henaff , Joan Bruna , and Yann LeCun . 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 ( 2015 ). Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)."},{"key":"e_1_3_2_1_24_1","volume-title":"USENIX Security Symposium. 1895--1912","author":"Jayaraman Bargav","year":"2019","unstructured":"Bargav Jayaraman and David Evans . 2019 . Evaluating differentially private machine learning in practice . In USENIX Security Symposium. 1895--1912 . Bargav Jayaraman and David Evans. 2019. Evaluating differentially private machine learning in practice. In USENIX Security Symposium. 1895--1912."},{"key":"e_1_3_2_1_25_1","volume-title":"Asian Conference on Machine Learning.","author":"Ji Tianxi","year":"2019","unstructured":"Tianxi Ji , Changqing Luo , Yifan Guo , Jinlong Ji , Weixian Liao , and Pan Li . 2019 . Differentially private community detection in attributed social networks . In Asian Conference on Machine Learning. Tianxi Ji, Changqing Luo, Yifan Guo, Jinlong Ji, Weixian Liao, and Pan Li. 2019. Differentially private community detection in attributed social networks. In Asian Conference on Machine Learning."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33627-0_21"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36594-2_26"},{"key":"e_1_3_2_1_28_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_29_1","volume-title":"LPGNet: Link Private Graph Networks for Node Classification. arXiv preprint arXiv:2205.03105","author":"Kolluri Aashish","year":"2022","unstructured":"Aashish Kolluri , Teodora Baluta , Bryan Hooi , and Prateek Saxena . 2022. LPGNet: Link Private Graph Networks for Node Classification. arXiv preprint arXiv:2205.03105 ( 2022 ). Aashish Kolluri, Teodora Baluta, Bryan Hooi, and Prateek Saxena. 2022. LPGNet: Link Private Graph Networks for Node Classification. arXiv preprint arXiv:2205.03105 (2022)."},{"key":"e_1_3_2_1_30_1","volume-title":"Private Hierarchical Clustering in Federated Networks. arXiv preprint arXiv:2105.09057","author":"Kolluri Aashish","year":"2021","unstructured":"Aashish Kolluri , Teodora Baluta , and Prateek Saxena . 2021. Private Hierarchical Clustering in Federated Networks. arXiv preprint arXiv:2105.09057 ( 2021 ). Aashish Kolluri, Teodora Baluta, and Prateek Saxena. 2021. Private Hierarchical Clustering in Federated Networks. arXiv preprint arXiv:2105.09057 (2021)."},{"key":"e_1_3_2_1_31_1","unstructured":"Oliver Lange and Luis Perez. 2020. Traffic prediction with advanced Graph Neural Networks. https:\/\/deepmind.com\/blog\/article\/traffic-prediction-with-advanced-graph-neural-networks  Oliver Lange and Luis Perez. 2020. Traffic prediction with advanced Graph Neural Networks. https:\/\/deepmind.com\/blog\/article\/traffic-prediction-with-advanced-graph-neural-networks"},{"key":"e_1_3_2_1_32_1","volume-title":"Gradient-based learning applied to document recognition. Proc","author":"LeCun Yann","year":"1998","unstructured":"Yann LeCun , L\u00e9on Bottou , Yoshua Bengio , and Patrick Haffner . 1998. Gradient-based learning applied to document recognition. Proc . IEEE ( 1998 ). Yann LeCun, L\u00e9on Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE (1998)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623683"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Xiaorui Liu Tong Zhao Yozen Liu Jiliang Tang and Neil Shah. 2021. A Unified View on Graph Neural Networks as Graph Signal Denoising.  Yao Ma Xiaorui Liu Tong Zhao Yozen Liu Jiliang Tang and Neil Shah. 2021. A Unified View on Graph Neural Networks as Graph Signal Denoising.","DOI":"10.1145\/3459637.3482225"},{"key":"e_1_3_2_1_35_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson , Lynn Smith-Lovin , and James M Cook . 2001. Birds of a feather: Homophily in social networks. Annual review of sociology ( 2001 ). Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology (2001)."},{"key":"e_1_3_2_1_36_1","volume-title":"Do you even need attention? a stack of feed-forward layers does surprisingly well on imagenet. arXiv preprint arXiv:2105.02723","author":"Melas-Kyriazi Luke","year":"2021","unstructured":"Luke Melas-Kyriazi . 2021. Do you even need attention? a stack of feed-forward layers does surprisingly well on imagenet. arXiv preprint arXiv:2105.02723 ( 2021 ). Luke Melas-Kyriazi. 2021. Do you even need attention? a stack of feed-forward layers does surprisingly well on imagenet. arXiv preprint arXiv:2105.02723 (2021)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2320765.2320818"},{"key":"e_1_3_2_1_38_1","volume-title":"EDBT\/ICDT Workshops.","author":"M\u00fclle Yvonne","year":"2015","unstructured":"Yvonne M\u00fclle , Chris Clifton , and Klemens B\u00f6hm . 2015 . Privacy-Integrated Graph Clustering Through Differential Privacy .. In EDBT\/ICDT Workshops. Yvonne M\u00fclle, Chris Clifton, and Klemens B\u00f6hm. 2015. Privacy-Integrated Graph Clustering Through Differential Privacy.. In EDBT\/ICDT Workshops."},{"key":"e_1_3_2_1_39_1","volume-title":"Adversary instantiation: Lower bounds for differentially private machine learning. arXiv preprint arXiv:2101.04535","author":"Nasr Milad","year":"2021","unstructured":"Milad Nasr , Shuang Song , Abhradeep Thakurta , Nicolas Papernot , and Nicholas Carlini . 2021. Adversary instantiation: Lower bounds for differentially private machine learning. arXiv preprint arXiv:2101.04535 ( 2021 ). Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini. 2021. Adversary instantiation: Lower bounds for differentially private machine learning. arXiv preprint arXiv:2101.04535 (2021)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2994620.2994624"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1250790.1250803"},{"key":"e_1_3_2_1_42_1","volume-title":"Yu Lei, and Bo Yang.","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei , Bingzhe Wei , Kevin Chen-Chuan Chang , Yu Lei, and Bo Yang. 2020 . Geom-gcn : Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287 (2020). Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287 (2020)."},{"key":"e_1_3_2_1_43_1","volume-title":"abs\/1708.06145","author":"Pyrgelis Apostolos","year":"2018","unstructured":"Apostolos Pyrgelis , Carmela Troncoso , and Emiliano De Cristofaro . 2018. Knock Knock , Who's There? Membership Inference on Aggregate Location Data . ArXiv , Vol. abs\/1708.06145 ( 2018 ). Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro. 2018. Knock Knock, Who's There? Membership Inference on Aggregate Location Data. ArXiv , Vol. abs\/1708.06145 (2018)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134086"},{"key":"e_1_3_2_1_45_1","volume-title":"Efficient lipschitz extensions for high-dimensional graph statistics and node private degree distributions. arXiv preprint arXiv:1504.07912","author":"Raskhodnikova Sofya","year":"2015","unstructured":"Sofya Raskhodnikova and Adam Smith . 2015. Efficient lipschitz extensions for high-dimensional graph statistics and node private degree distributions. arXiv preprint arXiv:1504.07912 ( 2015 ). Sofya Raskhodnikova and Adam Smith. 2015. Efficient lipschitz extensions for high-dimensional graph statistics and node private degree distributions. arXiv preprint arXiv:1504.07912 (2015)."},{"key":"e_1_3_2_1_46_1","volume-title":"Multi-Scale Attributed Node Embedding. Journal of Complex Networks","author":"Rozemberczki Benedek","year":"2021","unstructured":"Benedek Rozemberczki , Carl Allen , and Rik Sarkar . 2021a. Multi-Scale Attributed Node Embedding. Journal of Complex Networks ( 2021 ). Benedek Rozemberczki, Carl Allen, and Rik Sarkar. 2021a. Multi-Scale Attributed Node Embedding. Journal of Complex Networks (2021)."},{"key":"e_1_3_2_1_47_1","volume-title":"Multi-scale attributed node embedding. Journal of Complex Networks","author":"Rozemberczki Benedek","year":"2021","unstructured":"Benedek Rozemberczki , Carl Allen , and Rik Sarkar . 2021b. Multi-scale attributed node embedding. Journal of Complex Networks ( 2021 ). Benedek Rozemberczki, Carl Allen, and Rik Sarkar. 2021b. Multi-scale attributed node embedding. Journal of Complex Networks (2021)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2068816.2068825"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"e_1_3_2_1_50_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. 2008 . The graph neural network model. IEEE transactions on neural networks (2008). Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks (2008)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_30"},{"key":"e_1_3_2_1_52_1","volume-title":"Mlp-mixer: An all-mlp architecture for vision. arXiv preprint arXiv:2105.01601","author":"Tolstikhin Ilya","year":"2021","unstructured":"Ilya Tolstikhin , Neil Houlsby , Alexander Kolesnikov , Lucas Beyer , Xiaohua Zhai , Thomas Unterthiner , Jessica Yung , Daniel Keysers , Jakob Uszkoreit , Mario Lucic , 2021 . Mlp-mixer: An all-mlp architecture for vision. arXiv preprint arXiv:2105.01601 (2021). Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, et al. 2021. Mlp-mixer: An all-mlp architecture for vision. arXiv preprint arXiv:2105.01601 (2021)."},{"key":"e_1_3_2_1_53_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\u0107 , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_55_1","volume-title":"Preserving differential privacy in degree-correlation based graph generation. Transactions on data privacy","author":"Wang Yue","year":"2013","unstructured":"Yue Wang and Xintao Wu. 2013. Preserving differential privacy in degree-correlation based graph generation. Transactions on data privacy ( 2013 ). Yue Wang and Xintao Wu. 2013. Preserving differential privacy in degree-correlation based graph generation. Transactions on data privacy (2013)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37456-2_28"},{"key":"e_1_3_2_1_57_1","volume-title":"Asgldp: Collecting and generating decentralized attributed graphs with local differential privacy","author":"Wei Chengkun","year":"2020","unstructured":"Chengkun Wei , Shouling Ji , Changchang Liu , Wenzhi Chen , and Ting Wang . 2020 . Asgldp: Collecting and generating decentralized attributed graphs with local differential privacy . IEEE Transactions on Information Forensics and Security ( 2020). Chengkun Wei, Shouling Ji, Changchang Liu, Wenzhi Chen, and Ting Wang. 2020. Asgldp: Collecting and generating decentralized attributed graphs with local differential privacy. IEEE Transactions on Information Forensics and Security (2020)."},{"key":"e_1_3_2_1_58_1","volume-title":"Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925","author":"Wu Chuhan","year":"2021","unstructured":"Chuhan Wu , Fangzhao Wu , Yang Cao , Yongfeng Huang , and Xing Xie . 2021 b. Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021). Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie. 2021b. Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021)."},{"key":"e_1_3_2_1_59_1","volume-title":"LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis. arXiv preprint arXiv:2108.06504","author":"Wu Fan","year":"2021","unstructured":"Fan Wu , Yunhui Long , Ce Zhang , and Bo Li. 2021a. LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis. arXiv preprint arXiv:2108.06504 ( 2021 ). Fan Wu, Yunhui Long, Ce Zhang, and Bo Li. 2021a. LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis. arXiv preprint arXiv:2108.06504 (2021)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623642"},{"key":"e_1_3_2_1_62_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019. How powerful are graph neural networks? ICLR ( 2019 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_64_1","volume-title":"T-gcn: A temporal graph convolutional network for traffic prediction","author":"Zhao Ling","year":"2019","unstructured":"Ling Zhao , Yujiao Song , Chao Zhang , Yu Liu , Pu Wang , Tao Lin , Min Deng , and Haifeng Li . 2019 . T-gcn: A temporal graph convolutional network for traffic prediction . IEEE Transactions on Intelligent Transportation Systems ( 2019). Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2019. T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems (2019)."},{"key":"e_1_3_2_1_65_1","volume-title":"Privacy-preserving graph neural network for node classification. arXiv e-prints","author":"Zhou Jun","year":"2020","unstructured":"Jun Zhou , Chaochao Chen , Longfei Zheng , Xiaolin Zheng , Bingzhe Wu , Ziqi Liu , and Li Wang . 2020. Privacy-preserving graph neural network for node classification. arXiv e-prints ( 2020 ), arXiv--2005. Jun Zhou, Chaochao Chen, Longfei Zheng, Xiaolin Zheng, Bingzhe Wu, Ziqi Liu, and Li Wang. 2020. Privacy-preserving graph neural network for node classification. arXiv e-prints (2020), arXiv--2005."},{"key":"e_1_3_2_1_66_1","volume-title":"Beyond homophily in graph neural networks: Current limitations and effective designs. NeurIPS","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. NeurIPS ( 2020 ). io Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. NeurIPS (2020). io"}],"event":{"name":"CCS '22: 2022 ACM SIGSAC Conference on Computer and Communications Security","location":"Los Angeles CA USA","acronym":"CCS '22","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548606.3560705","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3548606.3560705","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:05Z","timestamp":1750182665000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548606.3560705"}},"subtitle":["Link Private Graph Networks for Node Classification"],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":66,"alternative-id":["10.1145\/3548606.3560705","10.1145\/3548606"],"URL":"https:\/\/doi.org\/10.1145\/3548606.3560705","relation":{},"subject":[],"published":{"date-parts":[[2022,11,7]]},"assertion":[{"value":"2022-11-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}