{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:08Z","timestamp":1750220288839,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"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":[],"published-print":{"date-parts":[[2021,9,24]]},"DOI":"10.1145\/3488933.3488975","type":"proceedings-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T11:36:59Z","timestamp":1645789019000},"page":"687-697","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Graph Embedding Method Based on Opinion Dynamics"],"prefix":"10.1145","author":[{"given":"DuJinwei","family":"DuJinwei","sequence":"first","affiliation":[{"name":"Nanjing University Of Finance and Economics, China"}]},{"given":"BuZhan","family":"BuZhan","sequence":"additional","affiliation":[{"name":"Nanjing University Of Finance and Economics, China"}]},{"given":"YiTao","family":"YiTao","sequence":"additional","affiliation":[{"name":"Nanjing University Of Finance and Economics, China"}]}],"member":"320","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.4304\/jait.2.1.40-49"},{"key":"e_1_3_2_1_2_1","volume-title":"Computer Science","author":"Bhagat S.","year":"2011","unstructured":"S. Bhagat , G. Cormode , and S. Muthukrishnan . Node Classification in Social Networks . Computer Science , 2011 . S. Bhagat, G. Cormode, and S. Muthukrishnan. Node Classification in Social Networks. Computer Science, 2011."},{"issue":"7","key":"e_1_3_2_1_3_1","article-title":"link prediction problem for social networks","volume":"58","author":"The","year":"2003","unstructured":"The link prediction problem for social networks . Journal of the American Society for Information Science and Technology , 58 ( 7 ), 2003 . The link prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 2003.","journal-title":"Journal of the American Society for Information Science and Technology"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1109\/ICDM.2001.989507"},{"key":"e_1_3_2_1_6_1","first-page":"2012","article-title":"A new approach on cluster based call scheduling for mobile networks","volume":"3","author":"Guha Thakurta P K","year":"2012","unstructured":"P K Guha Thakurta , S. Basu , S. Goswami , and S. Bandyopadhyay . A new approach on cluster based call scheduling for mobile networks . Journal of Advances in Information Technology , 3 ,3( 2012 - 2008 -01), 3(3), 2012 . P K Guha Thakurta, S. Basu, S. Goswami, and S. Bandyopadhyay. A new approach on cluster based call scheduling for mobile networks. Journal of Advances in Information Technology,3,3(2012-08-01), 3(3), 2012.","journal-title":"Journal of Advances in Information Technology"},{"issue":"86","key":"e_1_3_2_1_7_1","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . Visualizing data using t-sne . Journal of Machine Learning Research , 9 ( 86 ): 2579 \u2013 2605 , 2008 . Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(86):2579\u20132605, 2008.","journal-title":"Journal of Machine Learning Research"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1126\/science.290.5500.2323"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1007\/11556121_81"},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on International Conference on Machine Learning","author":"Luo D.","year":"2011","unstructured":"D. Luo , Chq Ding , F. Nie , and H. Huang . Cauchy graph embedding . In International Conference on International Conference on Machine Learning , 2011 . D. Luo, Chq Ding, F. Nie, and H. Huang. Cauchy graph embedding. In International Conference on International Conference on Machine Learning, 2011."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1145\/1553374.1553494"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1145\/2488388.2488393"},{"key":"e_1_3_2_1_13_1","first-page":"900","volume-title":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM \u201915","author":"Cao Shaosheng","year":"2015","unstructured":"Shaosheng Cao , Wei Lu , and Qiongkai Xu. Grarep : Learning graph representations with global structural information . In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM \u201915 , page 891\u2013 900 , New York, NY, USA , 2015 . Association for Computing Machinery. Shaosheng Cao, Wei Lu, and Qiongkai Xu. Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM \u201915, page 891\u2013900, New York, NY, USA, 2015. Association for Computing Machinery."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1145\/2939672.2939751"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1109\/TKDE.2007.46"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1016\/j.socnet.2004.11.009"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1145\/2939672.2939754"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1145\/2623330.2623732"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1080\/0022250X.1990.9990069"},{"issue":"3","key":"e_1_3_2_1_20_1","first-page":"2","article-title":"Opinion dynamics and bounded confidence models, analysis and simulation","volume":"5","author":"Hegselmann R.","year":"2002","unstructured":"R. Hegselmann . Opinion dynamics and bounded confidence models, analysis and simulation . Journal of Artificial Societies & Social Simulation , 5 ( 3 ): 2 , 2002 . R. Hegselmann. Opinion dynamics and bounded confidence models, analysis and simulation. Journal of Artificial Societies & Social Simulation, 5(3):2, 2002.","journal-title":"Journal of Artificial Societies & Social Simulation"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.1142\/S0219525900000078"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1146\/annurev.psych.55.090902.142015"},{"key":"e_1_3_2_1_23_1","first-page":"1","article-title":"Graph k-means based on leader identification, dynamic game and opinion dynamics","author":"Bu Z.","year":"2019","unstructured":"Z. Bu , Hui Jia Li , C. Zhang , J. Cao , Anhua Li , and Y. Shi . Graph k-means based on leader identification, dynamic game and opinion dynamics . IEEE Transactions on Knowledge and Data Engineering, pages 1 \u2013 1 , 2019 . Z. Bu, Hui Jia Li, C. Zhang, J. Cao, Anhua Li, and Y. Shi. Graph k-means based on leader identification, dynamic game and opinion dynamics. IEEE Transactions on Knowledge and Data Engineering, pages 1\u20131, 2019.","journal-title":"IEEE Transactions on Knowledge and Data Engineering, pages"},{"key":"e_1_3_2_1_24_1","volume-title":"and Y. Bengio. Graph attention networks","author":"Velikovi Petar","year":"2018","unstructured":"Petar Velikovi , G. Cucurull , A. Casanova , A. Romero , P Lio `, and Y. Bengio. Graph attention networks . 2018 . Petar Velikovi, G. Cucurull, A. Casanova, A. Romero, P Lio`, and Y. Bengio. Graph attention networks. 2018."},{"key":"e_1_3_2_1_25_1","volume-title":"Computer Science","author":"Bruna J.","year":"2013","unstructured":"J. Bruna , W. Zaremba , A. Szlam , and Y. Lecun . Spectral networks and locally connected networks on graphs . Computer Science , 2013 . J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun. Spectral networks and locally connected networks on graphs. Computer Science, 2013."},{"key":"e_1_3_2_1_26_1","volume-title":"Computer Science","author":"Henaff M.","year":"2015","unstructured":"M. Henaff , J. Bruna , and Y. Lecun . Deep convolutional networks on graph-structured data . Computer Science , 2015 . M. Henaff, J. Bruna, and Y. Lecun. Deep convolutional networks on graph-structured data. Computer Science, 2015."},{"key":"e_1_3_2_1_27_1","volume-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"Defferrard Michal","year":"2016","unstructured":"Michal Defferrard , X. Bresson , and P. Vandergheynst . Convolutional neural networks on graphs with fast localized spectral filtering . 2016 . Michal Defferrard, X. Bresson, and P. Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. 2016."},{"key":"e_1_3_2_1_28_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 , 2017 . Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2017."},{"key":"e_1_3_2_1_29_1","volume-title":"Graph wavelet neural network","author":"Xu B.","year":"2019","unstructured":"B. Xu , H. Shen , Q. Cao , Y. Qiu , and X. Cheng . Graph wavelet neural network . 2019 . B. Xu, H. Shen, Q. Cao, Y. Qiu, and X.Cheng. Graph wavelet neural network. 2019."},{"key":"e_1_3_2_1_30_1","volume-title":"Convolutional networks on graphs for learning molecular fingerprints","author":"Duvenaud D.","year":"2015","unstructured":"D. Duvenaud , D. Maclaurin , J. Aguilera-Iparraguirre , R G\u00b4omez-Bombarelli , T. Hirzel , A. AspuruGuzik , and Ryan P Adams . Convolutional networks on graphs for learning molecular fingerprints . MIT Press , 2015 . D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R G\u00b4omez-Bombarelli, T. Hirzel, A. AspuruGuzik, and Ryan P Adams. Convolutional networks on graphs for learning molecular fingerprints. MIT Press, 2015."},{"key":"e_1_3_2_1_31_1","first-page":"2023","volume-title":"International conference on machine learning","author":"Niepert Mathias","unstructured":"Mathias Niepert , Mohamed Ahmed , and Konstantin Kutzkov . Learning convolutional neural networks for graphs . In International conference on machine learning , pages 2014\u2013 2023 . PMLR, 2016. Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. Learning convolutional neural networks for graphs. In International conference on machine learning, pages 2014\u20132023. PMLR, 2016."},{"key":"e_1_3_2_1_32_1","volume-title":"Geometric deep learning on graphs and manifolds using mixture model cnns.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Monti F.","year":"2017","unstructured":"F. Monti , D. Boscaini , J. Masci , E. Rodola , and M. M. Bronstein . Geometric deep learning on graphs and manifolds using mixture model cnns.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017 . F. Monti, D. Boscaini, J. Masci, E. Rodola, and M. M. Bronstein. Geometric deep learning on graphs and manifolds using mixture model cnns.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_1_34_1","first-page":"48","volume-title":"International conference on machine learning","author":"Yang Zhilin","unstructured":"Zhilin Yang , William Cohen , and Ruslan Salakhudinov . Revisiting semi-supervised learning with graph embeddings . In International conference on machine learning , pages 40\u2013 48 . PMLR, 2016. Zhilin Yang, William Cohen, and Ruslan Salakhudinov. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning, pages 40\u201348. PMLR, 2016."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_35_1","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_36_1","volume-title":"Computer Science","author":"Kingma D.","year":"2014","unstructured":"D. Kingma and J. Ba . Adam: A method for stochastic optimization . Computer Science , 2014 . D. Kingma and J. Ba. Adam: A method for stochastic optimization. Computer Science, 2014."},{"key":"e_1_3_2_1_37_1","first-page":"256","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics","author":"Glorot Xavier","unstructured":"Xavier Glorot and Yoshua Bengio . Understanding the difficulty of training deep feedforward neural networks . In Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages 249\u2013 256 . JMLR Workshop and Conference Proceedings, 2010. Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249\u2013256. JMLR Workshop and Conference Proceedings, 2010."},{"issue":"2605","key":"e_1_3_2_1_38_1","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Maaten Laurens Van Der","year":"2008","unstructured":"Van Der Maaten Laurens and Geoffrey Hinton . Visualizing data using t-sne . Journal of Machine Learning Research , 9 ( 2605 ): 2579 \u2013 2605 , 2008 . Van Der Maaten Laurens and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(2605):2579\u20132605, 2008.","journal-title":"Journal of Machine Learning Research"}],"event":{"acronym":"AIPR 2021","name":"AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern Recognition","location":"Xiamen China"},"container-title":["2021 4th International Conference on Artificial Intelligence and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488933.3488975","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488933.3488975","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:28Z","timestamp":1750188688000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488933.3488975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,24]]},"references-count":37,"alternative-id":["10.1145\/3488933.3488975","10.1145\/3488933"],"URL":"https:\/\/doi.org\/10.1145\/3488933.3488975","relation":{},"subject":[],"published":{"date-parts":[[2021,9,24]]},"assertion":[{"value":"2022-02-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}