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Internet Technol."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications. Since social media images are usually accompanied by rich metadata (e.g., textual descriptions, tags, groups, and submitted users), simply modeling the image is not effective to learn the comprehensive information from social media images. In this work, we treat the image and its textual description as multimodal content, and transform other metainformation into the links between contents (such as two images marked by the same tag or submitted by the same user). Based on the multimodal content and social links, we propose a\n            <jats:bold>Deep Attentive Multimodal Graph Embedding<\/jats:bold>\n            model named\n            <jats:bold>DAMGE<\/jats:bold>\n            for more effective social image representation learning. We introduce both small- and large-scale datasets to conduct extensive experiments, of which the results confirm the superiority of the proposal on the tasks of social image classification and link prediction.\n          <\/jats:p>","DOI":"10.1145\/3417295","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T14:51:35Z","timestamp":1623855095000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Attentive\u00a0Multimodal Network Representation Learning for Social Media Images"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-0212","authenticated-orcid":false,"given":"Feiran","family":"Huang","sequence":"first","affiliation":[{"name":"College of Cyber Security, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research at Asia, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyu","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Cyber Security, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sattam","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Head of Innovation and Entrepreneurship Center, College of Engineering, Taif University, Taif, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780317"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2016.06.001"},{"key":"e_1_2_1_3_1","volume-title":"6th International Conference on Learning Representations (ICLR\u201918)","author":"Chen Jie","year":"2018","unstructured":"Jie Chen , Tengfei Ma , and Cao Xiao . 2018 . 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