{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:59:15Z","timestamp":1786089555377,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["NSFC No. 61772302 61732008"],"award-info":[{"award-number":["NSFC No. 61772302 61732008"]}]},{"name":"National Key Research and Development Program of China","award":["No. 2018YFB1004503"],"award-info":[{"award-number":["No. 2018YFB1004503"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403140","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:03:59Z","timestamp":1597964639000},"page":"976-985","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":236,"title":["Adaptive Graph Encoder for Attributed Graph Embedding"],"prefix":"10.1145","author":[{"given":"Ganqu","family":"Cui","sequence":"first","affiliation":[{"name":"Tsinghua University &amp; Beijing National Research Center for Information Science and Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Tsinghua University &amp; Beijing National Research Center for Information Science and Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University &amp; Beijing National Research Center for Information Science and Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_2_2_1","volume-title":"Proceedings of AAAI. 2738--2745","author":"Bojchevski Aleksandar","year":"2018","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann . 2018 . Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure . In Proceedings of AAAI. 2738--2745 . Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure. In Proceedings of AAAI. 2738--2745."},{"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","volume-title":"Proceedings of AAAI. 1145--1152","author":"Cao Shaosheng","year":"2016","unstructured":"Shaosheng Cao , Wei Lu , and Qiongkai Xu . 2016 . Deep neural networks for learning graph representations . In Proceedings of AAAI. 1145--1152 . Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In Proceedings of AAAI. 1145--1152."},{"key":"e_1_3_2_2_5_1","unstructured":"Jonathan Chang and David Blei. 2009. Relational topic models for document networks. In Artificial Intelligence and Statistics. 81--88.  Jonathan Chang and David Blei. 2009. Relational topic models for document networks. In Artificial Intelligence and Statistics. 81--88."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.626"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Fan RK Chung and Fan Chung Graham. 1997. Spectral graph theory. American Mathematical Soc.  Fan RK Chung and Fan Chung Graham. 1997. Spectral graph theory. American Mathematical Soc.","DOI":"10.1090\/cbms\/092"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"e_1_3_2_2_9_1","volume-title":"Data clustering: Theory, algorithms, and applications","author":"Gan Guojun","unstructured":"Guojun Gan , Chaoqun Ma , and Jianhong Wu. 2007. Data clustering: Theory, algorithms, and applications . Vol. 20 . Siam . Guojun Gan, Chaoqun Ma, and Jianhong Wu. 2007. Data clustering: Theory, algorithms, and applications. Vol. 20. Siam."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_11_1","volume-title":"Representation learning on graphs: Methods and applications","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton , Rex Ying , and Jure Leskovec . 2017. Representation learning on graphs: Methods and applications . IEEE Data(base) Engineering Bulletin 40, 3 ( 2017 ), 52--74. William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. IEEE Data(base) Engineering Bulletin 40, 3 (2017), 52--74."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.035102"},{"key":"e_1_3_2_2_13_1","volume-title":"Matrix analysis","author":"Horn Roger A","unstructured":"Roger A Horn and Charles R Johnson . 2012. Matrix analysis . Cambridge university press . Roger A Horn and Charles R Johnson. 2012. Matrix analysis. Cambridge university press."},{"key":"e_1_3_2_2_14_1","volume-title":"Proceedings of ICLR. 15","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization . In Proceedings of ICLR. 15 . Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of ICLR. 15."},{"key":"e_1_3_2_2_15_1","volume-title":"NIPS Workshop on Bayesian Deep Learning. 3.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016 . Variational graph auto-encoders . In NIPS Workshop on Bayesian Deep Learning. 3. Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. In NIPS Workshop on Bayesian Deep Learning. 3."},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of ICLR. 14","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and MaxWelling. 2017 . Semi-supervised classification with graph convolutional networks . In Proceedings of ICLR. 14 . Thomas N Kipf and MaxWelling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR. 14."},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings of ICML. 1188--1196","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov . 2014 . Distributed representations of sentences and documents . In Proceedings of ICML. 1188--1196 . Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of ICML. 1188--1196."},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of AAAI. 3538--3545","author":"Li Qimai","year":"2018","unstructured":"Qimai Li , Zhichao Han , and Xiao-Ming Wu . 2018 . Deeper insights into graph convolutional networks for semi-supervised learning . In Proceedings of AAAI. 3538--3545 . Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of AAAI. 3538--3545."},{"key":"e_1_3_2_2_19_1","volume-title":"Proceedings of AAAI. 338--345","author":"Li Ye","year":"2018","unstructured":"Ye Li , Chaofeng Sha , Xin Huang , and Yanchun Zhang . 2018 . Community detection in attributed graphs: an embedding approach . In Proceedings of AAAI. 338--345 . Ye Li, Chaofeng Sha, Xin Huang, and Yanchun Zhang. 2018. Community detection in attributed graphs: an embedding approach. In Proceedings of AAAI. 338--345."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_3_2_2_21_1","volume-title":"Finding community structure in networks using the eigenvectors of matrices. Physical review E 74, 3","author":"Newman Mark EJ","year":"2006","unstructured":"Mark EJ Newman . 2006. Finding community structure in networks using the eigenvectors of matrices. Physical review E 74, 3 ( 2006 ), 036--104. Mark EJ Newman. 2006. Finding community structure in networks using the eigenvectors of matrices. Physical review E 74, 3 (2006), 036--104."},{"key":"e_1_3_2_2_22_1","volume-title":"Proceedings of NIPS. 849--856","author":"Ng Andrew Y","year":"2002","unstructured":"Andrew Y Ng , Michael I Jordan , and Yair Weiss . 2002 . On spectral clustering: Analysis and an algorithm . In Proceedings of NIPS. 849--856 . Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002. On spectral clustering: Analysis and an algorithm. In Proceedings of NIPS. 849--856."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00662"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_26_1","volume-title":"Collective classification in network data. AI magazine 29, 3","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Galligher , and Tina Eliassi-Rad . 2008. Collective classification in network data. AI magazine 29, 3 ( 2008 ), 93--93. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218473"},{"key":"e_1_3_2_2_29_1","volume-title":"Accelerating t-SNE using tree-based algorithms. The journal of machine learning research 15, 1","author":"Der Maaten Laurens Van","year":"2014","unstructured":"Laurens Van Der Maaten . 2014. Accelerating t-SNE using tree-based algorithms. The journal of machine learning research 15, 1 ( 2014 ), 3221--3245. Laurens Van Der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. The journal of machine learning research 15, 1 (2014), 3221--3245."},{"key":"e_1_3_2_2_30_1","volume-title":"Proceedings of ICLR. 8.","author":"Cucurull Guillem","year":"2018","unstructured":", Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph attention networks . In Proceedings of ICLR. 8. , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of ICLR. 8."},{"key":"e_1_3_2_2_31_1","volume-title":"Proceedings of IJCAI. 3670--3676","author":"Pan Shirui","year":"2019","unstructured":"ChunWang, Shirui Pan , Ruiqi Hu , Guodong Long , Jing Jiang , and Chengqi Zhang . 2019 . Attributed graph clustering: A deep attentional embedding approach . In Proceedings of IJCAI. 3670--3676 . ChunWang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Attributed graph clustering: A deep attentional embedding approach. In Proceedings of IJCAI. 3670--3676."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132967"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of AAAI. 265--271","author":"Wang Xiao","year":"2016","unstructured":"Xiao Wang , Di Jin , Xiaochun Cao , Liang Yang , and Weixiong Zhang . 2016 . Semantic community identification in large attribute networks . In Proceedings of AAAI. 265--271 . Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, and Weixiong Zhang. 2016. Semantic community identification in large attribute networks. In Proceedings of AAAI. 265--271."},{"key":"e_1_3_2_2_35_1","volume-title":"Proceedings of ICML. 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Tianyi Zhang , Amauri Holanda de Souza Jr , Christopher Fifty , Tao Yu , and Kilian Q Weinberger . 2019 . Simplifying graph convolutional networks . In Proceedings of ICML. 6861--6871 . Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of ICML. 6861--6871."},{"key":"e_1_3_2_2_36_1","volume-title":"Proceedings of IJCAI. 2111--2117","author":"Yang Cheng","year":"2015","unstructured":"Cheng Yang , Zhiyuan Liu , Deli Zhao , Maosong Sun , and Edward Chang . 2015 . Network representation learning with rich text information . In Proceedings of IJCAI. 2111--2117 . Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Chang. 2015. Network representation learning with rich text information. In Proceedings of IJCAI. 2111--2117."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/601"},{"key":"e_1_3_2_2_39_1","volume-title":"Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434","author":"Zhou Jie","year":"2018","unstructured":"Jie Zhou , Ganqu Cui , Zhengyan Zhang , Cheng Yang , Zhiyuan Liu , Lifeng Wang , Changcheng Li , and Maosong Sun . 2018. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434 ( 2018 ). Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)."}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","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 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403140","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:34Z","timestamp":1750195894000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":39,"alternative-id":["10.1145\/3394486.3403140","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403140","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}