{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:12:12Z","timestamp":1774894332313,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T00:00:00Z","timestamp":1531958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1302675, 1344152, 1356628, 1619308, 1633753"],"award-info":[{"award-number":["1302675, 1344152, 1356628, 1619308, 1633753"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000098","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["AG049371"],"award-info":[{"award-number":["AG049371"]}],"id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,7,19]]},"DOI":"10.1145\/3219819.3220041","type":"proceedings-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T13:05:12Z","timestamp":1532005512000},"page":"1406-1415","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Self-Paced Network Embedding"],"prefix":"10.1145","author":[{"given":"Hongchang","family":"Gao","sequence":"first","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart'\u0131n Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard et almbox. . {n. d.}. TensorFlow: A System for Large-Scale Machine Learning.  Mart'\u0131n Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard et almbox. . {n. d.}. TensorFlow: A System for Large-Scale Machine Learning."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_3_1","volume-title":"et almbox","author":"Breitkreutz Bobby-Joe","year":"2008"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_1_5_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio . 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680.   Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio . 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188396"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.71"},{"key":"e_1_3_2_1_9_1","unstructured":"Lu Jiang Deyu Meng Shoou-I Yu Zhenzhong Lan Shiguang Shan and Alexander Hauptmann . 2014. Self-paced learning with diversity. In Advances in Neural Information Processing Systems. 2078--2086.   Lu Jiang Deyu Meng Shoou-I Yu Zhenzhong Lan Shiguang Shan and Alexander Hauptmann . 2014. Self-paced learning with diversity. In Advances in Neural Information Processing Systems. 2078--2086."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 2694--2700","author":"Jiang Lu","year":"2015"},{"key":"e_1_3_2_1_11_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik","year":"2014"},{"key":"e_1_3_2_1_12_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016"},{"key":"e_1_3_2_1_13_1","unstructured":"M Pawan Kumar Benjamin Packer and Daphne Koller . 2010. Self-paced learning for latent variable models. In Advances in Neural Information Processing Systems. 1189--1197.   M Pawan Kumar Benjamin Packer and Daphne Koller . 2010. Self-paced learning for latent variable models. In Advances in Neural Information Processing Systems. 1189--1197."},{"key":"e_1_3_2_1_14_1","unstructured":"Jure Leskovec and Andrej Krevl . 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data. (June . 2014).  Jure Leskovec and Andrej Krevl . 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data. (June . 2014)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132919"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. 2275--2284","author":"Ma Fan","year":"2017"},{"key":"e_1_3_2_1_17_1","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_18_1","volume-title":"Large text compression benchmark. URL: http:\/\/www. mattmahoney. net\/text\/text. html","author":"Matt Mahoney","year":"2009"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_20_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean . 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119.   Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean . 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098061"},{"key":"e_1_3_2_1_23_1","unstructured":"John Schulman Nicolas Heess Theophane Weber and Pieter Abbeel . 2015. Gradient estimation using stochastic computation graphs Advances in Neural Information Processing Systems. 3528--3536.   John Schulman Nicolas Heess Theophane Weber and Pieter Abbeel . 2015. Gradient estimation using stochastic computation graphs Advances in Neural Information Processing Systems. 3528--3536."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.308"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Peng Cui Jing Wang Jian Pei Wenwu Zhu and Shiqiang Yang . 2017 a. Community Preserving Network Embedding. (2017).  Xiao Wang Peng Cui Jing Wang Jian Pei Wenwu Zhu and Shiqiang Yang . 2017 a. Community Preserving Network Embedding. (2017).","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"e_1_3_2_1_30_1","unstructured":"Cheng Yang Zhiyuan Liu Deli Zhao Maosong Sun and Edward Y Chang . {n. d.}. Network Representation Learning with Rich Text Information.  Cheng Yang Zhiyuan Liu Deli Zhao Maosong Sun and Edward Y Chang . {n. d.}. Network Representation Learning with Rich Text Information."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484126"}],"event":{"name":"KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"London United Kingdom","acronym":"KDD '18","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 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3219819.3220041","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3219819.3220041","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3219819.3220041","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:07:29Z","timestamp":1750212449000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3219819.3220041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,19]]},"references-count":31,"alternative-id":["10.1145\/3219819.3220041","10.1145\/3219819"],"URL":"https:\/\/doi.org\/10.1145\/3219819.3220041","relation":{},"subject":[],"published":{"date-parts":[[2018,7,19]]},"assertion":[{"value":"2018-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}