{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:19:57Z","timestamp":1791047997620,"version":"4.1.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U20B2045, 62192784, 62172052, 62002029, 62172052, U1936104"],"award-info":[{"award-number":["No. U20B2045, 62192784, 62172052, 62002029, 62172052, U1936104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583282","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"251-262","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Post-Training Framework for Improving Heterogeneous Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7821-0030","authenticated-orcid":false,"given":"Cheng","family":"Yang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9104-1582","authenticated-orcid":false,"given":"Xumeng","family":"Gong","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-0266","authenticated-orcid":false,"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip","family":"Yu","sequence":"additional","affiliation":[{"name":"UNIVERSITY OF ILLINOIS AT CHICAGO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of SIGKDD.","author":"Fan Yujie","year":"2018","unstructured":"Yujie Fan, Shifu Hou, Yiming Zhang, Yanfang Ye, and Melih Abdulhayoglu. 2018. Gotcha-sly malware! scorpion a metagraph2vec based malware detection system. In Proceedings of SIGKDD."},{"key":"e_1_3_2_1_3_1","volume-title":"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In WWW.","author":"Fu Xinyu","year":"2020","unstructured":"Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In WWW."},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of ICML.","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel\u00a0S Schoenholz, Patrick\u00a0F Riley, Oriol Vinyals, and George\u00a0E Dahl. 2017. Neural message passing for quantum chemistry. In Proceedings of ICML."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of ICML.","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel\u00a0S. Schoenholz, Patrick\u00a0F. Riley, Oriol Vinyals, and George\u00a0E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In Proceedings of ICML."},{"key":"e_1_3_2_1_6_1","volume-title":"Hawk: Rapid android malware detection through heterogeneous graph attention networks","author":"Hei Yiming","year":"2021","unstructured":"Yiming Hei, Renyu Yang, Hao Peng, Lihong Wang, Xiaolin Xu, Jianwei Liu, Hong Liu, Jie Xu, and Lichao Sun. 2021. Hawk: Rapid android malware detection through heterogeneous graph attention networks. IEEE TNNLS (2021)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Huiting Hong Hantao Guo Yucheng Lin Xiaoqing Yang Zang Li and Jieping Ye. 2020. An Attention-Based Graph Neural Network for Heterogeneous Structural Learning. In AAAI.","DOI":"10.1609\/aaai.v34i04.5833"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Shifu Hou Yanfang Ye Yangqiu Song and Melih Abdulhayoglu. 2017. HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network. In KDD.","DOI":"10.1145\/3097983.3098026"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449914"},{"key":"e_1_3_2_1_11_1","volume-title":"Kipf and Max Welling","author":"N.","year":"2017","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of ICLR."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467429"},{"key":"e_1_3_2_1_13_1","unstructured":"Yuanfu Lu Yuan Fang and Chuan Shi. 2020. Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. In KDD Rajesh Gupta Yan Liu Jiliang Tang and B.\u00a0Aditya Prakash (Eds.)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"e_1_3_2_1_15_1","volume-title":"Gmnn: Graph markov neural networks. In ICML.","author":"Qu Meng","year":"2019","unstructured":"Meng Qu, Yoshua Bengio, and Jian Tang. 2019. Gmnn: Graph markov neural networks. In ICML."},{"key":"e_1_3_2_1_16_1","volume-title":"Heterogeneous Deep Graph Infomax. arxiv","author":"Ren Yuxiang","year":"2019","unstructured":"Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, and Jiawei Zhang. 2019. Heterogeneous Deep Graph Infomax. arxiv (2019)."},{"key":"e_1_3_2_1_17_1","volume-title":"The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA","author":"Shi Chuan","year":"2021","unstructured":"Chuan Shi, Yuan Fang, Yanfang Ye, and Jiawei Zhang. 2021. The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021). In KDD, Feida Zhu, Beng\u00a0Chin Ooi, and Chunyan Miao (Eds.)."},{"key":"e_1_3_2_1_18_1","volume-title":"Mining heterogeneous information networks: a structural analysis approach. Acm Sigkdd Explorations Newsletter","author":"Sun Yizhou","year":"2013","unstructured":"Yizhou Sun and Jiawei Han. 2013. Mining heterogeneous information networks: a structural analysis approach. Acm Sigkdd Explorations Newsletter (2013)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554901"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of ICLR.","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In Proceedings of ICLR."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_1_23_1","unstructured":"Fengli Xu Jianxun Lian Zhenyu Han Yong Li Yujian Xu and Xing Xie. 2019. Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In CIKM."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Cheng Yang Jiawei Liu and Chuan Shi. 2021. Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework. In WWW.","DOI":"10.1145\/3442381.3450068"},{"key":"e_1_3_2_1_25_1","volume":"201","author":"Yun Seongjun","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo\u00a0J. Kim. 2019. Graph Transformer Networks. In Proceedings of NeurIPS.","journal-title":"J. Kim."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389706"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16600"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/190"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Jiawei Zheng Qianli Ma Hao Gu and Zhenjing Zheng. 2021. Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation. In KDD Feida Zhu Beng\u00a0Chin Ooi and Chunyan Miao (Eds.).","DOI":"10.1145\/3447548.3467427"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00203"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583282","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:22Z","timestamp":1750178242000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":31,"alternative-id":["10.1145\/3543507.3583282","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583282","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}