{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T21:06:59Z","timestamp":1768252019195,"version":"3.49.0"},"publisher-location":"New York, New York, USA","reference-count":22,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1145\/3184558.3191563","type":"proceedings-article","created":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T18:04:25Z","timestamp":1524074665000},"page":"1243-1251","source":"Crossref","is-referenced-by-count":3,"title":["<i>A3embed<\/i>"],"prefix":"10.1145","author":[{"given":"Jihwan","family":"Lee","sequence":"first","affiliation":[{"name":"Amazon Alexa Brain & Purdue University, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Prabhakar","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/3184558.3191563-1","doi-asserted-by":"crossref","unstructured":"Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828.","DOI":"10.1109\/TPAMI.2013.50"},{"key":"key-10.1145\/3184558.3191563-2","unstructured":"Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep Neural Networks for Learning Graph Representations. AAAI. 1145--1152."},{"key":"key-10.1145\/3184558.3191563-3","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855--864.","DOI":"10.1145\/2939672.2939754"},{"key":"key-10.1145\/3184558.3191563-4","doi-asserted-by":"crossref","unstructured":"Xiao Huang, Jundong Li, and Xia Hu. 2017 a. Accelerated attributed network embedding. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 633--641.","DOI":"10.1137\/1.9781611974973.71"},{"key":"key-10.1145\/3184558.3191563-5","doi-asserted-by":"crossref","unstructured":"Xiao Huang, Jundong Li, and Xia Hu. 2017 b. Label informed attributed network embedding. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 731--739.","DOI":"10.1145\/3018661.3018667"},{"key":"key-10.1145\/3184558.3191563-6","unstructured":"Myunghwan Kim and Jure Leskovec. 2012. Multiplicative attribute graph model of real-world networks. Internet Mathematics, Vol. 8, 1--2 (2012), 113--160."},{"key":"key-10.1145\/3184558.3191563-7","unstructured":"Jihwan Lee, Keehwan Park, and Sunil Prabhakar. 2016. Mining Statistically Significant Attribute Associations in Attributed Graphs IEEE 16th International Conference on Data Mining (ICDM). IEEE, 991--996."},{"key":"key-10.1145\/3184558.3191563-8","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research Vol. 9, Nov (2008), 2579--2605."},{"key":"key-10.1145\/3184558.3191563-9","doi-asserted-by":"crossref","unstructured":"Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology Vol. 27, 1 (2001), 415--444.","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"key-10.1145\/3184558.3191563-10","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)."},{"key":"key-10.1145\/3184558.3191563-11","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 b. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119."},{"key":"key-10.1145\/3184558.3191563-12","doi-asserted-by":"crossref","unstructured":"Krzysztof Nowicki and Tom A B Snijders. 2001. Estimation and prediction for stochastic blockstructures. J. Amer. Statist. Assoc. Vol. 96, 455 (2001), 1077--1087.","DOI":"10.1198\/016214501753208735"},{"key":"key-10.1145\/3184558.3191563-13","unstructured":"Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. 2016 a. Tri-party Deep Network Representation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16). AAAI Press, 1895--1901. http:\/\/dl.acm.org\/citation.cfmid=3060832.3060886"},{"key":"key-10.1145\/3184558.3191563-14","unstructured":"Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. 2016 b. Tri-Party Deep Network Representation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9--15 July2016. 1895--1901. http:\/\/www.ijcai.org\/Abstract\/16\/271"},{"key":"key-10.1145\/3184558.3191563-15","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 701--710.","DOI":"10.1145\/2623330.2623732"},{"key":"key-10.1145\/3184558.3191563-16","doi-asserted-by":"crossref","unstructured":"Everett M Rogers and Dilip K Bhowmik. 1970. Homophily-heterophily: Relational concepts for communication research. Public opinion quarterly Vol. 34, 4 (1970), 523--538.","DOI":"10.1086\/267838"},{"key":"key-10.1145\/3184558.3191563-17","doi-asserted-by":"crossref","unstructured":"Ruslan Salakhutdinov and Geoffrey Hinton. 2009. Semantic hashing. International Journal of Approximate Reasoning, Vol. 50, 7 (2009), 969--978.","DOI":"10.1016\/j.ijar.2008.11.006"},{"key":"key-10.1145\/3184558.3191563-18","doi-asserted-by":"crossref","unstructured":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067--1077.","DOI":"10.1145\/2736277.2741093"},{"key":"key-10.1145\/3184558.3191563-19","unstructured":"T. Tieleman and G. Hinton. 2012. Lecture 6.5--RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning. (2012)."},{"key":"key-10.1145\/3184558.3191563-20","doi-asserted-by":"crossref","unstructured":"Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1225--1234.","DOI":"10.1145\/2939672.2939753"},{"key":"key-10.1145\/3184558.3191563-21","doi-asserted-by":"crossref","unstructured":"Yuchung J Wang and George Y Wong. 1987. Stochastic blockmodels for directed graphs. J. Amer. Statist. Assoc. Vol. 82, 397 (1987), 8--19.","DOI":"10.1080\/01621459.1987.10478385"},{"key":"key-10.1145\/3184558.3191563-22","unstructured":"Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. 2015. Network representation learning with rich text information Proceedings of the 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina. 2111--2117."}],"event":{"name":"Companion of the The Web Conference 2018","location":"Lyon, France","acronym":"WWW '18","number":"2018","sponsor":["IW3C2, International World Wide Web Conference Committee","SIGWEB, ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"start":{"date-parts":[[2018,4,23]]},"end":{"date-parts":[[2018,4,27]]}},"container-title":["Companion of the The Web Conference 2018 on The Web Conference 2018  - WWW '18"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3184558.3191563","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3191563&ftid=1958299&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:08:31Z","timestamp":1750208911000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3184558.3191563"}},"subtitle":["Attribute Association Aware Network Embedding"],"proceedings-subject":"The Web Conference 2018","short-title":[],"issued":{"date-parts":[[2018]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1145\/3184558.3191563","relation":{},"subject":[],"published":{"date-parts":[[2018]]}}}