{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T10:17:07Z","timestamp":1782209827573,"version":"3.54.5"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            User counterparts, such as user attributes in social networks or user interests, are the keys to more natural\n            <jats:bold>Human\u2013Computer Interaction (HCI)<\/jats:bold>\n            . In addition, users\u2019 attributes and social structures help us understand the complex interactions in HCI. Most previous studies have been based on supervised learning to improve the performance of HCI. However, in the real world, owing to signal malfunctions in user devices, large amounts of abnormal information, unlabeled data, and unsupervised approaches (e.g., the clustering method) based on mining user attributes are particularly crucial. This paper focuses on improving the clustering performance of users\u2019 attributes in HCI and proposes a\n            <jats:bold>deep graph embedding network with feature and structure similarity<\/jats:bold>\n            (called\n            <jats:bold>DGENFS<\/jats:bold>\n            ) to cluster users\u2019 attributes in HCI applications based on feature and structure similarity. The DGENFS model consists of a\n            <jats:bold>Feature Graph Autoencoder (FGA)<\/jats:bold>\n            module, a\n            <jats:bold>Structure Graph Attention Network (SGAT)<\/jats:bold>\n            module, and a\n            <jats:bold>Dual Self-supervision (DSS)<\/jats:bold>\n            module. First, we design an attributed graph clustering method to divide users into clusters by making full use of their attributes. To take full advantage of the information of human feature space, a k-neighbor graph is generated as a feature graph based on the similarity between human features. Then, the FGA and SGAT modules are utilized to extract the representations of human features and topological space, respectively. Next, an attention mechanism is further developed to learn the importance weights of different representations to effectively integrate human features and social structures. Finally, to learn cluster-friendly features, the DSS module unifies and integrates the features learned from the FGA and SGAT modules. DSS explores the high-confidence cluster assignment as a soft label to guide the optimization of the entire network. Extensive experiments are conducted on five real-world data sets on user attribute clustering. The experimental results demonstrate that the proposed DGENFS model achieves the most advanced performance compared with nine competitive baselines.\n          <\/jats:p>","DOI":"10.1145\/3549954","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T11:15:19Z","timestamp":1661339719000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Graph Network with Multiple Similarity for User Clustering in Human\u2013Computer Interaction"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6969-0562","authenticated-orcid":false,"given":"Yan","family":"Kang","sequence":"first","affiliation":[{"name":"Yunnan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4959-8432","authenticated-orcid":false,"given":"Bin","family":"Pu","sequence":"additional","affiliation":[{"name":"Hunan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8662-6340","authenticated-orcid":false,"given":"Yongqi","family":"Kou","sequence":"additional","affiliation":[{"name":"Yunnan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9893-3436","authenticated-orcid":false,"given":"Yun","family":"Yang","sequence":"additional","affiliation":[{"name":"Yunnan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5009-578X","authenticated-orcid":false,"given":"Jianguo","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5302-1150","authenticated-orcid":false,"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8553-7127","authenticated-orcid":false,"given":"Po","family":"Yang","sequence":"additional","affiliation":[{"name":"Sheffield University, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-5217","authenticated-orcid":false,"given":"Lida","family":"Xu","sequence":"additional","affiliation":[{"name":"Old Dominion University, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9279-401X","authenticated-orcid":false,"given":"Mohammad","family":"Hijji","sequence":"additional","affiliation":[{"name":"University of Tabuk, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1314303.1314306"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2645860"},{"key":"e_1_3_1_4_2","volume-title":"Man-Machine Engineering.","author":"Chapanis Alphonse","year":"1965","unstructured":"Alphonse Chapanis. 1965. Man-Machine Engineering.Wadsworth Pub. Co., Inc."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/576915"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01238028"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJAACS.2008.019799"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2019.2919702"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Stina Nylander Jakob Tholander Florian Mueller and Joe Marshall. 2014. HCI and sports. (2014) 115\u2013118.","DOI":"10.1145\/2559206.2559223"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-016-0519-4"},{"issue":"5","key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/TCYB.2018.2809562","article-title":"Adaptive bi-weighting toward automatic initialization and model selection for HMM-based hybrid meta-clustering ensembles","volume":"49","author":"Yang Yun","year":"2018","unstructured":"Yun Yang and Jianmin Jiang. 2018. Adaptive bi-weighting toward automatic initialization and model selection for HMM-based hybrid meta-clustering ensembles. IEEE Transactions on Cybernetics 49, 5 (2018), 1657\u20131668.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"5","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TNNLS.2015.2430821","article-title":"Hybrid sampling-based clustering ensemble with global and local constitutions","volume":"27","author":"Yang Yun","year":"2015","unstructured":"Yun Yang and Jianmin Jiang. 2015. Hybrid sampling-based clustering ensemble with global and local constitutions. IEEE Transactions on Neural Networks and Learning Systems 27, 5 (2015), 952\u2013965.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.11.022"},{"issue":"2","key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"10\u2013es","DOI":"10.1145\/1230812.1230816","article-title":"Clustering and searching WWW images using link and page layout analysis","volume":"3","author":"He Xiaofei","year":"2007","unstructured":"Xiaofei He, Deng Cai, Ji-Rong Wen, Wei-Ying Ma, and Hong-Jiang Zhang. 2007. Clustering and searching WWW images using link and page layout analysis. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 3, 2 (2007), 10\u2013es.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"issue":"3","key":"e_1_3_1_15_2","first-page":"1","article-title":"Features-enhanced multi-attribute estimation with convolutional tensor correlation fusion network","volume":"15","author":"Duan Mingxing","year":"2019","unstructured":"Mingxing Duan, Kenli Li, Xiangke Liao, Keqin Li, and Qi Tian. 2019. Features-enhanced multi-attribute estimation with convolutional tensor correlation fusion network. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 15, 3s (2019), 1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2993148.2997632"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2501643.2501647"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3133944.3133949"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.03.105"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Fei Tian Bin Gao Qing Cui Enhong Chen and Tie-Yan Liu. 2014. Learning deep representations for graph clustering. 28 1 (2014).","DOI":"10.1609\/aaai.v28i1.8916"},{"key":"e_1_3_1_24_2","unstructured":"Junyuan Xie Ross Girshick and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. (2016) 478\u2013487."},{"key":"e_1_3_1_25_2","first-page":"1753","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Guo Xifeng","year":"2017","unstructured":"Xifeng Guo, Long Gao, Xinwang Liu, and Jianping Yin. 2017. Improved deep embedded clustering with local structure preservation. In Proceedings of the International Joint Conference on Artificial Intelligence. 1753\u20131759."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Jian Tang Meng Qu Mingzhe Wang Ming Zhang Jun Yan and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. (2015) 1067\u20131077.","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_1_28_2","article-title":"BAG: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering","author":"Cao Yu","year":"2019","unstructured":"Yu Cao, Meng Fang, and Dacheng Tao. 2019. BAG: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering. arXiv preprint arXiv:1904.04969 (2019).","journal-title":"arXiv preprint arXiv:1904.04969"},{"key":"e_1_3_1_29_2","article-title":"How powerful are graph neural networks?","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018).","journal-title":"arXiv preprint arXiv:1810.00826"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_1_31_2","first-page":"5453","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In Proceedings of the International Conference on Machine Learning. PMLR, 5453\u20135462."},{"key":"e_1_3_1_32_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"e_1_3_1_33_2","article-title":"Variational graph auto-encoders","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).","journal-title":"arXiv preprint arXiv:1611.07308"},{"key":"e_1_3_1_34_2","article-title":"Attributed graph clustering: A deep attentional embedding approach","author":"Wang Chun","year":"2019","unstructured":"Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906.06532 (2019).","journal-title":"arXiv preprint arXiv:1906.06532"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2848470"},{"key":"e_1_3_1_36_2","unstructured":"Shaosheng Cao Wei Lu and Qiongkai Xu. 2015. GraRep: Learning graph representations with global structural information. (2015) 891\u2013900."},{"key":"e_1_3_1_37_2","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi Rami Al-Rfou and Steven Skiena. 2014. DeepWalk: Online learning of social representations. (2014) 701\u2013710.","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"e_1_3_1_39_2","first-page":"3155","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","volume":"18","author":"Zhang Zhen","year":"2018","unstructured":"Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. 2018. ANRL: Attributed network representation learning via deep neural networks. In Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 18. 3155\u20133161."},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132967"},{"key":"e_1_3_1_41_2","first-page":"arXiv\u20132002","article-title":"Embedding graph auto-encoder with joint clustering via adjacency sharing","author":"Li Xuelong","year":"2020","unstructured":"Xuelong Li, Hongyuan Zhang, and Rui Zhang. 2020. Embedding graph auto-encoder with joint clustering via adjacency sharing. arXiv e-prints (2020), arXiv\u20132002.","journal-title":"arXiv e-prints"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380214"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.05.026"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/2502433"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"Shengping Zhang Huiyu Zhou Dong Xu M. Emre Celebi and Thierry Bouwmans. 2020. Introduction to the Special Issue on Multimodal Machine Learning for Human Behavior Analysis. (2020).","DOI":"10.1145\/3381917"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/2457450.2457451"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3131344"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/2502436"},{"key":"e_1_3_1_49_2","first-page":"230","volume-title":"Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT)","author":"Nguyen Duc Son","year":"2020","unstructured":"Duc Son Nguyen and Quynh Mai Le. 2020. Hacking user in human-computer interaction design (HCI). In Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT). IEEE, 230\u2013234."},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/ICCSNT.2015.7490772","volume-title":"Proceedings of the 2015 4th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"1","author":"Shen Wei","year":"2015","unstructured":"Wei Shen and Xiaolei Zhou. 2015. Research on the human-computer interaction mode designed for elderly users. In Proceedings of the 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), Vol. 1. 374\u2013377."},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/IUSER.2016.7857933","volume-title":"Proceedings of the 2016 4th International Conference on User Science and Engineering (I-User)","author":"Adeyemi Ikuesan R.","year":"2016","unstructured":"Ikuesan R. Adeyemi, Shukor Abd Razak, and Mazleena Salleh. 2016. Individual difference for HCI systems: Examining the probability of thinking style signature in online interaction. In Proceedings of the 2016 4th International Conference on User Science and Engineering (I-User). IEEE, 51\u201356."},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2020.3017784"},{"key":"e_1_3_1_53_2","volume-title":"Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic","author":"Ng Andrew","year":"2001","unstructured":"Andrew Ng, Michael Jordan, and Yair Weiss. 2001. On spectral clustering: Analysis and an algorithm. In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"issue":"2605","key":"e_1_3_1_55_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Laurens Van Der Maaten","year":"2008","unstructured":"Van Der Maaten Laurens and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2605 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of the Association for the Advance of Artificial Intelligence","author":"Nikolentzos Giannis","year":"2017","unstructured":"Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis. 2017. Matching node embeddings for graph similarity. In Proceedings of the Association for the Advance of Artificial Intelligence."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.035102"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549954","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3549954","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:14Z","timestamp":1750183694000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3549954"],"URL":"https:\/\/doi.org\/10.1145\/3549954","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]},"assertion":[{"value":"2021-11-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}