{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T07:12:56Z","timestamp":1777187576635,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"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":["IIS-1447795"],"award-info":[{"award-number":["IIS-1447795"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Army Research Laboratory","award":["W911NF-09-2-0053"],"award-info":[{"award-number":["W911NF-09-2-0053"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330961","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"793-803","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1187,"title":["Heterogeneous Graph Neural Network"],"prefix":"10.1145","author":[{"given":"Chuxu","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Dongjin","family":"Song","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, Inc., Princeton, NJ, USA"}]},{"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, JD Digits, Notre Dame, IN, USA"}]},{"given":"Ananthram","family":"Swami","sequence":"additional","affiliation":[{"name":"US Army Research Laboratory, Adelphi, MD, USA"}]},{"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783296"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018735"},{"key":"e_1_3_2_1_3_1","volume-title":"A survey on network embedding. TKDE","author":"Cui Peng","year":"2018","unstructured":"Peng Cui , Xiao Wang , Jian Pei , and Wenwu Zhu . 2018. A survey on network embedding. TKDE ( 2018 ). Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018. A survey on network embedding. TKDE (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"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","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. NIPS. 1024--1034.   Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. NIPS. 1024--1034."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219965"},{"key":"e_1_3_2_1_11_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_12_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR .  Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR ."},{"key":"e_1_3_2_1_13_1","unstructured":"Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML. 1188--1196.   Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML. 1188--1196."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132919"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2819980"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272010"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Jonathan Long Evan Shelhamer and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In CVPR. 3431--3440.  Jonathan Long Evan Shelhamer and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In CVPR. 3431--3440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220062"},{"key":"e_1_3_2_1_19_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS. 3111--3119.   Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS. 3111--3119."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159711"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623630"},{"key":"e_1_3_2_1_24_1","volume-title":"Ivan Titov, and Max Welling.","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull , Thomas N Kipf , Peter Bloem , Rianne Van Den Berg , Ivan Titov, and Max Welling. 2018 . Modeling relational data with graph convolutional networks. In ESWC. 593--607. Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC. 593--607."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124373"},{"key":"e_1_3_2_1_26_1","first-page":"992","article-title":"Pathsim: Meta path-based top-k similarity search in heterogeneous information networks","volume":"4","author":"Sun Yizhou","year":"2011","unstructured":"Yizhou Sun , Jiawei Han , Xifeng Yan , Philip S Yu , and Tianyi Wu . 2011 . Pathsim: Meta path-based top-k similarity search in heterogeneous information networks . VLDB , Vol. 4 , 11 (2011), 992 -- 1003 . Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. VLDB, Vol. 4, 11 (2011), 992--1003.","journal-title":"VLDB"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339738"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783307"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_1_31_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR .  Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR ."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220000"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186152"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291001"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Chuxu Zhang Lu Yu Xiangliang Zhang and Nitesh V Chawla. 2018. Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference.. In IJCAI. 3641--3647.   Chuxu Zhang Lu Yu Xiangliang Zhang and Nitesh V Chawla. 2018. Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference.. In IJCAI. 3641--3647.","DOI":"10.24963\/ijcai.2018\/506"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186106"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","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 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330961","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330961","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330961","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:04Z","timestamp":1750206364000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":36,"alternative-id":["10.1145\/3292500.3330961","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330961","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}