{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:23:41Z","timestamp":1750220621320,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["4182062"],"award-info":[{"award-number":["4182062"]}]},{"name":"National Key Research and Development Project","award":["2017YFC0820700"],"award-info":[{"award-number":["2017YFC0820700"]}]},{"name":"National Natural Science Foundation of China","award":["61702470"],"award-info":[{"award-number":["61702470"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3411891","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T05:31:06Z","timestamp":1603085466000},"page":"1923-1932","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["LRHNE: A Latent-Relation Enhanced Embedding Method for Heterogeneous Information Networks"],"prefix":"10.1145","author":[{"given":"Zhihua","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences &amp; Institute of Computing Technology Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xinxin","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xiaokai","family":"Chu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences &amp; Institute of Computing Technology Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jianhui","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jingping","family":"Bi","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Wild","author":"Chen Bin","year":"2012","unstructured":"Bin Chen , Ying Ding , and David J . Wild . 2012 . Assessing Drug Target Association Using Semantic Linked Data . PLoS Computational Biology, Vol. 8 , 7 (2012). Bin Chen, Ying Ding, and David J. Wild. 2012. Assessing Drug Target Association Using Semantic Linked Data. PLoS Computational Biology, Vol. 8, 7 (2012)."},{"doi-asserted-by":"crossref","unstructured":"Ting Chen and Yizhou Sun. 2017. Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification. In WSDM. 295--304.  Ting Chen and Yizhou Sun. 2017. Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification. In WSDM. 295--304.","key":"e_1_3_2_2_2_1","DOI":"10.1145\/3018661.3018735"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_3_1","DOI":"10.1109\/TKDE.2018.2849727"},{"unstructured":"Micha\u00eb l Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurIPS. 3837--3845.  Micha\u00eb l Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurIPS. 3837--3845.","key":"e_1_3_2_2_4_1"},{"doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh V. Chawla and Ananthram Swami. 2017. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. In SIGKDD. 135--144.  Yuxiao Dong Nitesh V. Chawla and Ananthram Swami. 2017. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. In SIGKDD. 135--144.","key":"e_1_3_2_2_5_1","DOI":"10.1145\/3097983.3098036"},{"unstructured":"Tao-Yang Fu Wang-Chien Lee and Zhen Lei. 2017. HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. In CIKM. 135--144.  Tao-Yang Fu Wang-Chien Lee and Zhen Lei. 2017. HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. In CIKM. 135--144.","key":"e_1_3_2_2_6_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.","key":"e_1_3_2_2_7_1","DOI":"10.1145\/2939672.2939754"},{"doi-asserted-by":"crossref","unstructured":"Huan Gui Jialu Liu Fangbo Tao Meng Jiang Brandon Norick and Jiawei Han. 2016. Large-Scale Embedding Learning in Heterogeneous Event Data. In ICDM. 907--912.  Huan Gui Jialu Liu Fangbo Tao Meng Jiang Brandon Norick and Jiawei Han. 2016. Large-Scale Embedding Learning in Heterogeneous Event Data. In ICDM. 907--912.","key":"e_1_3_2_2_8_1","DOI":"10.1109\/ICDM.2016.0111"},{"unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034.  William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034.","key":"e_1_3_2_2_9_1"},{"unstructured":"Arman Hasanzadeh Ehsan Hajiramezanali Krishna R. Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Semi-Implicit Graph Variational Auto-Encoders. In NeurIPS. 10711--10722.  Arman Hasanzadeh Ehsan Hajiramezanali Krishna R. Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Semi-Implicit Graph Variational Auto-Encoders. In NeurIPS. 10711--10722.","key":"e_1_3_2_2_10_1"},{"unstructured":"Binbin Hu Yuan Fang and Chuan Shi. 2019. Adversarial Learning on Heterogeneous Information Networks. In SIGKDD. 120--129.  Binbin Hu Yuan Fang and Chuan Shi. 2019. Adversarial Learning on Heterogeneous Information Networks. In SIGKDD. 120--129.","key":"e_1_3_2_2_11_1"},{"key":"e_1_3_2_2_12_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes. In ICLR. http:\/\/arxiv.org\/abs\/1312.6114 Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In ICLR. http:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_2_2_13_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling . 2016 . Variational Graph Auto-Encoders. CoRR , Vol. abs\/ 1611 .07308 (2016). Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. CoRR, Vol. abs\/1611.07308 (2016)."},{"key":"e_1_3_2_2_14_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","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_2_15_1","volume-title":"Yu","author":"Kong Xiangnan","year":"2013","unstructured":"Xiangnan Kong , Bokai Cao , and Philip S . Yu . 2013 . Multi-label classification by mining label and instance correlations from heterogeneous information networks. In SIGKDD. 614--622. Xiangnan Kong, Bokai Cao, and Philip S. Yu. 2013. Multi-label classification by mining label and instance correlations from heterogeneous information networks. In SIGKDD. 614--622."},{"key":"e_1_3_2_2_16_1","volume-title":"Wild","author":"Kong Xiangnan","year":"2012","unstructured":"Xiangnan Kong , Philip S. Yu , Ying Ding , and David J . Wild . 2012 . Meta path-based collective classification in heterogeneous information networks. In CIKM. 1567--1571. Xiangnan Kong, Philip S. Yu, Ying Ding, and David J. Wild. 2012. Meta path-based collective classification in heterogeneous information networks. In CIKM. 1567--1571."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_17_1","DOI":"10.1023\/A:1009953814988"},{"doi-asserted-by":"crossref","unstructured":"Bryan Perozzi Rami Al-Rfou and Steven Skiena. 2014. DeepWalk: online learning of social representations. In SIGKDD. 701--710.  Bryan Perozzi Rami Al-Rfou and Steven Skiena. 2014. DeepWalk: online learning of social representations. In SIGKDD. 701--710.","key":"e_1_3_2_2_18_1","DOI":"10.1145\/2623330.2623732"},{"volume-title":"Eurographics Conference on Visualization. 73--77","author":"Rauber Paulo E.","unstructured":"Paulo E. Rauber , Alexandre X. Falc a o, and Alexandru C. Telea . 2016. Visualizing Time-Dependent Data Using Dynamic t-SNE . In Eurographics Conference on Visualization. 73--77 . Paulo E. Rauber, Alexandre X. Falc a o, and Alexandru C. Telea. 2016. Visualizing Time-Dependent Data Using Dynamic t-SNE. In Eurographics Conference on Visualization. 73--77.","key":"e_1_3_2_2_19_1"},{"key":"e_1_3_2_2_20_1","volume-title":"Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks. CoRR","author":"Shang Jingbo","year":"2016","unstructured":"Jingbo Shang , Meng Qu , Jialu Liu , Lance M. Kaplan , Jiawei Han , and Jian Peng . 2016. Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks. CoRR , Vol. abs\/ 1610 .09769 ( 2016 ). Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, and Jian Peng. 2016. Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks. CoRR, Vol. abs\/1610.09769 (2016)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_21_1","DOI":"10.1109\/TKDE.2016.2598561"},{"doi-asserted-by":"crossref","unstructured":"Yu Shi Huan Gui Qi Zhu Lance M. Kaplan and Jiawei Han. 2018a. AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks. In SDM. 144--152.  Yu Shi Huan Gui Qi Zhu Lance M. Kaplan and Jiawei Han. 2018a. AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks. In SDM. 144--152.","key":"e_1_3_2_2_22_1","DOI":"10.1137\/1.9781611975321.16"},{"doi-asserted-by":"crossref","unstructured":"Yu Shi Qi Zhu Fang Guo Chao Zhang and Jiawei Han. 2018b. Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks. In SIGKDD. 2190--2199.  Yu Shi Qi Zhu Fang Guo Chao Zhang and Jiawei Han. 2018b. Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks. In SIGKDD. 2190--2199.","key":"e_1_3_2_2_23_1","DOI":"10.1145\/3219819.3220006"},{"volume-title":"Mining Heterogeneous Information Networks: Principles and Methodologies","author":"Sun Yizhou","unstructured":"Yizhou Sun and Jiawei Han . 2012. Mining Heterogeneous Information Networks: Principles and Methodologies . Morgan & Claypool Publishers . Yizhou Sun and Jiawei Han. 2012. Mining Heterogeneous Information Networks: Principles and Methodologies .Morgan & Claypool Publishers.","key":"e_1_3_2_2_24_1"},{"doi-asserted-by":"crossref","unstructured":"Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott E. Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2015. Going deeper with convolutions. In CVPR. 1--9.  Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott E. Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2015. Going deeper with convolutions. In CVPR. 1--9.","key":"e_1_3_2_2_25_1","DOI":"10.1109\/CVPR.2015.7298594"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_26_1","DOI":"10.1145\/2783258.2783307"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_27_1","DOI":"10.1145\/2736277.2741093"},{"doi-asserted-by":"crossref","unstructured":"Daixin Wang Peng Cui and Wenwu Zhu. 2016. Structural Deep Network Embedding. In SIGKDD. 855--864.  Daixin Wang Peng Cui and Wenwu Zhu. 2016. Structural Deep Network Embedding. In SIGKDD. 855--864.","key":"e_1_3_2_2_28_1","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_2_29_1","volume-title":"Yu","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang , Houye Ji , Chuan Shi , Bai Wang , Yanfang Ye , Peng Cui , and Philip S . Yu . 2019 . Heterogeneous Graph Attention Network. In WWW. 2022--2032. Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous Graph Attention Network. In WWW. 2022--2032."},{"doi-asserted-by":"crossref","unstructured":"Yizhou Zhang Yun Xiong Xiangnan Kong Shanshan Li Jinhong Mi and Yangyong Zhu. 2018. Deep Collective Classification in Heterogeneous Information Networks. In WWW. 399--408.  Yizhou Zhang Yun Xiong Xiangnan Kong Shanshan Li Jinhong Mi and Yangyong Zhu. 2018. Deep Collective Classification in Heterogeneous Information Networks. In WWW. 399--408.","key":"e_1_3_2_2_30_1","DOI":"10.1145\/3178876.3186106"},{"key":"e_1_3_2_2_31_1","volume-title":"HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification. In KDD. https:\/\/doi.org\/10.1145\/3394486.3403169","author":"Zhu Zhihua","year":"2020","unstructured":"Zhihua Zhu , Xinxin Fan , Xiaokai Chu , and Jingping Bi . 2020 . HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification. In KDD. https:\/\/doi.org\/10.1145\/3394486.3403169 10.1145\/3394486.3403169 Zhihua Zhu, Xinxin Fan, Xiaokai Chu, and Jingping Bi. 2020. HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification. In KDD. https:\/\/doi.org\/10.1145\/3394486.3403169"}],"event":{"sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"acronym":"CIKM '20","name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Ireland"},"container-title":["Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411891","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3411891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:21Z","timestamp":1750197681000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":31,"alternative-id":["10.1145\/3340531.3411891","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3411891","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}