{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:05:22Z","timestamp":1760709922058,"version":"3.41.0"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T00:00:00Z","timestamp":1559260800000},"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":"crossref","award":["61432019, 61702509, 61802405, 61720106006, 61572503, 61772170 and 61632007"],"award-info":[{"award-number":["61432019, 61702509, 61802405, 61720106006, 61572503, 61772170 and 61632007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research Program of Frontier Sciences, CAS"},{"DOI":"10.13039\/501100012692","name":"K. C. Wong Education Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012692","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2019,5,31]]},"abstract":"<jats:p>\n            Network representation learning is playing an important role in network analysis due to its effectiveness in a variety of applications. However, most existing network embedding models focus on homogeneous networks and neglect the diverse properties such as different types of network structures and associated multimedia content information. In this article, we learn node representations for multimodal heterogeneous networks, which contain multiple types of nodes and\/or links as well as multimodal content such as texts and images. We propose a novel attention-aware collaborative multimodal heterogeneous network embedding method (A\n            <jats:sup>2<\/jats:sup>\n            CMHNE), where an attention-based collaborative representation learning approach is proposed to promote the collaboration of structure-based embedding and content-based embedding, and generate the robust node representation by introducing an attention mechanism that enables informative embedding integration. In experiments, we compare our model with existing network embedding models on two real-world datasets. Our method leads to dramatic improvements in performance by 5%, and 9% compared with five state-of-the-art embedding methods on one benchmark (M10 Dataset), and on a multi-modal heterogeneous network dataset (WeChat dataset) for node classification, respectively. Experimental results demonstrate the effectiveness of our proposed method on both node classification and link prediction tasks.\n          <\/jats:p>","DOI":"10.1145\/3321506","type":"journal-article","created":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T12:28:42Z","timestamp":1559824122000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A\n            <sup>2<\/sup>\n            CMHNE"],"prefix":"10.1145","volume":"15","author":[{"given":"Jun","family":"Hu","sequence":"first","affiliation":[{"name":"HeFei University of Technology, Hefei, China"}]},{"given":"Shengsheng","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"}]},{"given":"Quan","family":"Fang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"}]},{"given":"Xueliang","family":"Liu","sequence":"additional","affiliation":[{"name":"HeFei University of Technology, Hefei, China"}]},{"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"HeFei University of Technology, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, and Peng Cheng Laboratory, ShenZhen, China"}]}],"member":"320","published-online":{"date-parts":[[2019,6,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Smriti Bhagat Graham Cormode and S. 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Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization . In Proceedings of the 3rd International Conference on Learning Representations (ICLR'15) San Diego, CA. http:\/\/arxiv.org\/abs\/1412.6980 Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR'15) San Diego, CA. http:\/\/arxiv.org\/abs\/1412.6980"},{"volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR'17)","author":"Thomas","key":"e_1_2_1_13_1","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks . In Proceedings of the 5th International Conference on Learning Representations (ICLR'17) , Toulon, France. https:\/\/openreview.net\/forum?id&equals;SJU4ayYgl Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR'17), Toulon, France. https:\/\/openreview.net\/forum?id&equals;SJU4ayYgl"},{"key":"e_1_2_1_14_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling . 2016 . Variational graph auto-encoders. CoRR abs\/1611.07308. arxiv:1611.07308http:\/\/arxiv.org\/abs\/1611.07308. Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. CoRR abs\/1611.07308. arxiv:1611.07308http:\/\/arxiv.org\/abs\/1611.07308."},{"volume-title":"Proceedings of the 31th International Conference on Machine Learning (ICML'14)","author":"Quoc","key":"e_1_2_1_15_1","unstructured":"Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents . In Proceedings of the 31th International Conference on Machine Learning (ICML'14) , Beijing, China. 1188--1196. http:\/\/jmlr.org\/proceedings\/papers\/v32\/le14.html. Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the 31th International Conference on Machine Learning (ICML'14), Beijing, China. 1188--1196. http:\/\/jmlr.org\/proceedings\/papers\/v32\/le14.html."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/1241540.1241551"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016","author":"Pan Shirui","year":"2016","unstructured":"Shirui Pan , Jia Wu , Xingquan Zhu , Chengqi Zhang , and Yang Wang . 2016 . Tri-party deep network representation . In Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 , New York, NY, 9- -15 July 2016. 1895--1901. http:\/\/www.ijcai.org\/Abstract\/16\/271. Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. 2016. Tri-party deep network representation. 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Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. 2017. CANE: Context-aware network embedding for relation modeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30-- August 4, Volume 1: Long Papers. 1722--1731."},{"key":"e_1_2_1_27_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . J. Mach. Learn. Res. 9 , Nov (2008), 2579 -- 2605 . Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, Nov (2008), 2579--2605.","journal-title":"J. Mach. Learn. 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