{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:33:46Z","timestamp":1780623226518,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467273","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:13:10Z","timestamp":1628748790000},"page":"1667-1676","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Privacy-Preserving Representation Learning on Graphs"],"prefix":"10.1145","author":[{"given":"Binghui","family":"Wang","sequence":"first","affiliation":[{"name":"Illinois Institute of Technology & Duke University, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Guo","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ang","family":"Li","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Li","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Alexander A Alemi Ian Fischer Joshua V Dillon and Kevin Murphy. 2017. Deep variational information bottleneck. In ICLR.  Alexander A Alemi Ian Fischer Joshua V Dillon and Kevin Murphy. 2017. Deep variational information bottleneck. In ICLR."},{"key":"e_1_3_2_2_2_1","unstructured":"Mohamed Ishmael Belghazi Aristide Baratin Sai Rajeshwar Sherjil Ozair Yoshua Bengio Aaron Courville and Devon Hjelm. 2018. Mutual information neural estimation. In ICML.  Mohamed Ishmael Belghazi Aristide Baratin Sai Rajeshwar Sherjil Ozair Yoshua Bengio Aaron Courville and Devon Hjelm. 2018. Mutual information neural estimation. In ICML."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_2_4_1","unstructured":"Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In NeurIPS.  Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In NeurIPS."},{"key":"e_1_3_2_2_5_1","volume-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS.","author":"Chen Xi","year":"2016","unstructured":"Xi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , and Pieter Abbeel . 2016 . Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS."},{"key":"e_1_3_2_2_6_1","volume-title":"CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information. In ICML.","author":"Cheng Pengyu","year":"2020","unstructured":"Pengyu Cheng , Weituo Hao , Shuyang Dai , Jiachang Liu , Zhe Gan , and Lawrence Carin . 2020 . CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information. In ICML. Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. 2020. CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information. In ICML."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Ganqu Cui Jie Zhou Cheng Yang and Zhiyuan Liu. 2020. Adaptive Graph Encoder for Attributed Graph Embedding. In KDD.  Ganqu Cui Jie Zhou Cheng Yang and Zhiyuan Liu. 2020. Adaptive Graph Encoder for Attributed Graph Embedding. In KDD.","DOI":"10.1145\/3394486.3403140"},{"key":"e_1_3_2_2_8_1","unstructured":"Alberto Garcia Duran and Mathias Niepert. 2017. Learning graph representations with embedding propagation. In NIPS.  Alberto Garcia Duran and Mathias Niepert. 2017. Learning graph representations with embedding propagation. In NIPS."},{"key":"e_1_3_2_2_9_1","unstructured":"David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In NIPS.  David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In NIPS."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD.  Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_11_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS."},{"key":"e_1_3_2_2_12_1","unstructured":"R Devon Hjelm Alex Fedorov Samuel Lavoie-Marchildon Karan Grewal Phil Bachman Adam Trischler and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. In ICLR.  R Devon Hjelm Alex Fedorov Samuel Lavoie-Marchildon Karan Grewal Phil Bachman Adam Trischler and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. In ICLR."},{"key":"e_1_3_2_2_13_1","volume-title":"NIPS Workshop.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016 . Variational graph auto-encoders . In NIPS Workshop. Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. In NIPS Workshop."},{"key":"e_1_3_2_2_14_1","volume-title":"Semi-supervised classification with graph convolutional networks. ICLR","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017. Semi-supervised classification with graph convolutional networks. ICLR ( 2017 ). Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR (2017)."},{"key":"e_1_3_2_2_15_1","volume-title":"Hyperbolic geometry of complex networks. Physical Review E","author":"Krioukov Dmitri","year":"2010","unstructured":"Dmitri Krioukov , Fragkiskos Papadopoulos , Maksim Kitsak , Amin Vahdat , and Mari\u00e1n Bogun\u00e1 . 2010. Hyperbolic geometry of complex networks. Physical Review E ( 2010 ). Dmitri Krioukov, Fragkiskos Papadopoulos, Maksim Kitsak, Amin Vahdat, and Mari\u00e1n Bogun\u00e1. 2010. Hyperbolic geometry of complex networks. Physical Review E (2010)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Ang Li Yixiao Duan Huanrui Yang Yiran Chen and Jianlei Yang. 2020. TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations. In KDD.  Ang Li Yixiao Duan Huanrui Yang Yiran Chen and Jianlei Yang. 2020. TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations. In KDD.","DOI":"10.1145\/3394486.3403125"},{"key":"e_1_3_2_2_17_1","unstructured":"Qi Liu Maximilian Nickel and Douwe Kiela. 2019. Hyperbolic graph neural networks. In NeurIPS.  Qi Liu Maximilian Nickel and Douwe Kiela. 2019. Hyperbolic graph neural networks. In NeurIPS."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Suhang Wang Charu C Aggarwal and Jiliang Tang. 2019. Graph convolutional networks with eigenpooling. In KDD.  Yao Ma Suhang Wang Charu C Aggarwal and Jiliang Tang. 2019. Graph convolutional networks with eigenpooling. In KDD.","DOI":"10.1137\/1.9781611975673.74"},{"key":"e_1_3_2_2_19_1","unstructured":"Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In NIPS.  Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In NIPS."},{"key":"e_1_3_2_2_20_1","volume-title":"Representation learning with contrastive predictive coding. arXiv","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv ( 2018 ). Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv (2018)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Zhen Peng Wenbing Huang Minnan Luo Qinghua Zheng Yu Rong Tingyang Xu and Junzhou Huang. 2020. Graph Representation Learning via Graphical Mutual Information Maximization. In WWW.  Zhen Peng Wenbing Huang Minnan Luo Qinghua Zheng Yu Rong Tingyang Xu and Junzhou Huang. 2020. Graph Representation Learning via Graphical Mutual Information Maximization. In WWW.","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_23_1","unstructured":"Ben Poole Sherjil Ozair Aaron van den Oord Alexander A Alemi and George Tucker. 2019. On variational bounds of mutual information. In ICML.  Ben Poole Sherjil Ozair Aaron van den Oord Alexander A Alemi and George Tucker. 2019. On variational bounds of mutual information. In ICML."},{"key":"e_1_3_2_2_24_1","unstructured":"Jiezhong Qiu Yuxiao Dong Hao Ma Jian Li Kuansan Wang and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk line pte and node2vec. In WSDM.  Jiezhong Qiu Yuxiao Dong Hao Ma Jian Li Kuansan Wang and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk line pte and node2vec. In WSDM."},{"key":"e_1_3_2_2_25_1","volume-title":"GMNN: Graph Markov Neural Networks. In ICML.","author":"Qu Meng","year":"2019","unstructured":"Meng Qu , Yoshua Bengio , and Jian Tang . 2019 . GMNN: Graph Markov Neural Networks. In ICML. Meng Qu, Yoshua Bengio, and Jian Tang. 2019. GMNN: Graph Markov Neural Networks. In ICML."},{"key":"e_1_3_2_2_26_1","volume-title":"Pedro HP Saverese, and Daniel R Figueiredo","author":"Ribeiro Leonardo FR","year":"2017","unstructured":"Leonardo FR Ribeiro , Pedro HP Saverese, and Daniel R Figueiredo . 2017 . struc2vec: Learning node representations from structural identity. In KDD. Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo. 2017. struc2vec: Learning node representations from structural identity. In KDD."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Ryan A Rossi Rong Zhou and Nesreen K Ahmed. 2018. Deep inductive network representation learning. In WWW.  Ryan A Rossi Rong Zhou and Nesreen K Ahmed. 2018. Deep inductive network representation learning. In WWW.","DOI":"10.1145\/3184558.3191524"},{"key":"e_1_3_2_2_28_1","unstructured":"Prithviraj Sen Galileo Namata Mustafa Bilgic and etal 2008. Collective classification in network data. AI magazine (2008).  Prithviraj Sen Galileo Namata Mustafa Bilgic and et al. 2008. Collective classification in network data. AI magazine (2008)."},{"key":"e_1_3_2_2_29_1","volume-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR.","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun , Jordan Hoffmann , Vikas Verma , and Jian Tang . 2020 . Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR. Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_31_1","unstructured":"Cunchao Tu Weicheng Zhang Zhiyuan Liu and Maosong Sun. 2016. Max-margin DeepWalk: discriminative learning of network representation. In IJCAI.  Cunchao Tu Weicheng Zhang Zhiyuan Liu and Maosong Sun. 2016. Max-margin DeepWalk: discriminative learning of network representation. In IJCAI."},{"key":"e_1_3_2_2_32_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR.  Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR."},{"key":"e_1_3_2_2_33_1","unstructured":"Petar Velickovic William Fedus William L Hamilton Pietro Li\u00f2 Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax.. In ICLR.  Petar Velickovic William Fedus William L Hamilton Pietro Li\u00f2 Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax.. In ICLR."},{"key":"e_1_3_2_2_34_1","unstructured":"Felix Wu Tianyi Zhang Amauri Holanda de Souza Jr Christopher Fifty Tao Yu and Kilian Q Weinberger. 2019 b. Simplifying graph convolutional networks. In ICML.  Felix Wu Tianyi Zhang Amauri Holanda de Souza Jr Christopher Fifty Tao Yu and Kilian Q Weinberger. 2019 b. Simplifying graph convolutional networks. In ICML."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Jun Wu Jingrui He and Jiejun Xu. 2019 a. Demo-Net: Degree-specific graph neural networks for node and graph classification. In KDD.  Jun Wu Jingrui He and Jiejun Xu. 2019 a. Demo-Net: Degree-specific graph neural networks for node and graph classification. In KDD.","DOI":"10.1145\/3292500.3330950"},{"key":"e_1_3_2_2_36_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR.  Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR."},{"key":"e_1_3_2_2_37_1","unstructured":"Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In ICML.  Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In ICML."},{"key":"e_1_3_2_2_38_1","unstructured":"Zhilin Yang William Cohen and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML.  Zhilin Yang William Cohen and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML."},{"key":"e_1_3_2_2_39_1","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In NIPS.  Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In NIPS."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467273","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:28Z","timestamp":1750191508000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467273"}},"subtitle":["A Mutual Information Perspective"],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":39,"alternative-id":["10.1145\/3447548.3467273","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467273","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}