{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:12:18Z","timestamp":1774894338380,"version":"3.50.1"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tkde.2022.3185128","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T19:43:58Z","timestamp":1655840638000},"page":"1-13","source":"Crossref","is-referenced-by-count":8,"title":["Explicit Message-Passing Heterogeneous Graph Neural Network"],"prefix":"10.1109","author":[{"given":"Lei","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen-Yu","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9551-022X","authenticated-orcid":false,"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu-Qiang","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Cyber Security of Jinan University, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159666"},{"key":"ref35","article-title":"Deep graph library: Towards efficient and scalable deep learning on graphs","author":"wang","year":"2019","journal-title":"Proc 7th Int Conf Learn Representations Workshop"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1830252.1830270"},{"key":"ref34","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2792020"},{"key":"ref37","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2833443"},{"key":"ref36","first-page":"583","article-title":"Cluster ensembles &#x2014; A knowledge reuse framework for combining multiple partitions","volume":"3","author":"strehl","year":"2002","journal-title":"J Mach Learn Res"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1446-7.50035-2"},{"key":"ref30","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"weisfeiler","year":"1968","journal-title":"Nauchno- Technicheskaya Informatsia"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2997938"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15880-3_42"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2822307"},{"key":"ref32","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc 3rd Int Conf Learn Representations"},{"key":"ref2","article-title":"Gated graph sequence neural networks","author":"li","year":"2016","journal-title":"Proc 4th Int Conf Learn Representations"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2873391"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2373385"},{"key":"ref38","first-page":"3029","article-title":"A BP neural network based recommender framework with attention mechanism","volume":"34","author":"wang","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref19","first-page":"3391","article-title":"mSHINE: A multiple-meta-paths simultaneous learning framework for heterogeneous information network embedding","volume":"34","author":"zhang","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783296"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357924"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"ref26","article-title":"How powerful are graph neural networks?","author":"xu","year":"2019","journal-title":"Proc 7th Int Conf Learn Representations"},{"key":"ref25","first-page":"3837","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref21","article-title":"Relational graph attention networks","author":"busbridge","year":"2019"},{"key":"ref28","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"gilmer","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref27","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref29","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"ref7","article-title":"Graph attention networks","author":"velickovic","year":"2018","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56212-4"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref6","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref5","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc 5th Int Conf Learn Representations"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/4358933\/09802746.pdf?arnumber=9802746","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T02:09:11Z","timestamp":1686103751000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9802746\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/tkde.2022.3185128","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}