{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:43Z","timestamp":1760597983566,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s10489-020-01780-7","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T06:03:37Z","timestamp":1594015417000},"page":"3976-3989","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["FLGAI: a unified network embedding framework integrating multi-scale network structures and node attribute information"],"prefix":"10.1007","volume":"50","author":[{"given":"Yu","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyang","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsheng","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1870-0112","authenticated-orcid":false,"given":"Zhisong","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"issue":"8","key":"1780_CR1","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1109\/TSMC.2019.2899366","volume":"49","author":"J Cao","year":"2019","unstructured":"Cao J, Bu Z, Wang Y, Yang H, Jiang J, Li H-J (2019) Detecting prosumer-community groups in smart grids from the multiagent perspective. IEEE Trans Syst Man Cybern Syst 49(8):1652\u20131664. https:\/\/doi.org\/10.1109\/TSMC.2019.2899366","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"8","key":"1780_CR2","doi-asserted-by":"publisher","first-page":"5327","DOI":"10.1109\/TII.2019.2960835","volume":"16","author":"H-J Li","year":"2020","unstructured":"Li H-J, Bu Z, Wang Z, Cao J (2020) Dynamical clustering in electronic commerce systems via optimization and leadership expansion. IEEE Trans Ind Informatics 16(8):5327\u20135334. https:\/\/doi.org\/10.1109\/TII.2019.2960835","journal-title":"IEEE Trans Ind Informatics"},{"key":"1780_CR3","doi-asserted-by":"publisher","unstructured":"C NC, Mohan A (2019) A social recommender system using deep architecture and network embedding. Appl Intell 49(5):1937\u20131953. https:\/\/doi.org\/10.1007\/s10489-018-1359-z","DOI":"10.1007\/s10489-018-1359-z"},{"key":"1780_CR4","doi-asserted-by":"publisher","unstructured":"Tang J, Aggarwal C, Liu H (2016) Node classification in signed social networks. In: proceedings of the 2016 SIAM international conference on data mining. Proceedings. Society for Industrial and Applied Mathematics :54-62. https:\/\/doi.org\/10.1137\/1.9781611974348.710.1137\/1.9781611974348.7","DOI":"10.1137\/1.9781611974348.710.1137\/1.9781611974348.7"},{"key":"1780_CR5","doi-asserted-by":"publisher","unstructured":"Wang T, Liu L, Liu N, Zhang H, Zhang L, Feng S (2020) A multi-label text classification method via dynamic semantic representation model and deep neural network. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-020-01680-w","DOI":"10.1007\/s10489-020-01680-w"},{"key":"1780_CR6","doi-asserted-by":"publisher","unstructured":"Gao S, Denoyer L, Gallinari P (2011) Temporal link prediction by integrating content and structure information. https:\/\/doi.org\/10.1145\/2063576.2063744","DOI":"10.1145\/2063576.2063744"},{"key":"1780_CR7","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1145\/1401890.1401969","volume-title":"Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining","author":"AP Singh","year":"2008","unstructured":"Singh AP, Gordon GJ (2008) Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. Las Vegas, Nevada, USA, pp 650\u2013658"},{"key":"1780_CR8","doi-asserted-by":"publisher","unstructured":"Tang J, Liu J, Zhang M, Mei Q (2016) Visualizing large-scale and high-dimensional data. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11\u201315, 2016, pp. 287\u2013297. https:\/\/doi.org\/10.1145\/2872427.2883041","DOI":"10.1145\/2872427.2883041"},{"key":"1780_CR9","doi-asserted-by":"crossref","unstructured":"Li Y, Sha C, Huang X, Zhang Y (2018) Community detection in attributed graphs: an embedding approach. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, 2018, pp. 338\u2013345","DOI":"10.1609\/aaai.v32i1.11274"},{"issue":"1","key":"1780_CR10","doi-asserted-by":"crossref","first-page":"012801","DOI":"10.1103\/PhysRevE.91.012801","volume":"91","author":"HJ Li","year":"2015","unstructured":"Li HJ, Daniels JJ (2015) Social significance of community structure: statistical view. Phys Rev E 91(1):012801","journal-title":"Phys Rev E"},{"key":"1780_CR11","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"DeepWalk: online learning of social representations","author":"B Perozzi","year":"2014","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, In, pp 701\u2013710. https:\/\/doi.org\/10.1145\/2623330.2623732"},{"key":"1780_CR12","doi-asserted-by":"publisher","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, WWW 2015, Florence, Italy, pp. 1067\u20131077. https:\/\/doi.org\/10.1145\/2736277.2741093","DOI":"10.1145\/2736277.2741093"},{"key":"1780_CR13","doi-asserted-by":"publisher","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks, In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, USA , pp. 855\u2013864 .doi:https:\/\/doi.org\/10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"},{"key":"1780_CR14","doi-asserted-by":"crossref","unstructured":"Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4\u20139, 2017, San Francisco, California, USA, pp. 203\u2013209","DOI":"10.1609\/aaai.v31i1.10488"},{"issue":"1","key":"1780_CR15","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","volume":"27","author":"M McPherson","year":"2001","unstructured":"McPherson M, Smith-Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415\u2013444. https:\/\/doi.org\/10.1146\/annurev.soc.27.1.415","journal-title":"Annu Rev Sociol"},{"key":"1780_CR16","doi-asserted-by":"crossref","unstructured":"Zhang D, Yin J, Zhu X, Zhang C (2016) Homophily, structure, and content augmented network representation learning. In: Proceedings of the IEEE 16th international conference on data mining, ICDM2016, Barcelona, Spain, pp. 609\u2013618","DOI":"10.1109\/ICDM.2016.0072"},{"key":"1780_CR17","doi-asserted-by":"crossref","unstructured":"Yang D, Wang S, Li C, Zhang X, Li Z (2017) From properties to links: deep network embedding on incomplete graphs. In: Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM 2017, Singapore, pp 367\u2013376","DOI":"10.1145\/3132847.3132975"},{"key":"1780_CR18","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of the 14th international conference on neural information processing systems: natural and synthetic, Vancouver, British Columbia, Canada, pp. 585\u2013591"},{"key":"1780_CR19","doi-asserted-by":"crossref","unstructured":"Roweis, Sam, T., Saul, Lawrence, K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","DOI":"10.1126\/science.290.5500.2323"},{"key":"1780_CR20","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2015) GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM international on conference on information and knowledge management, Melbourne, Australia, pp. 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"1780_CR21","doi-asserted-by":"crossref","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA, pp. 1105\u20131114","DOI":"10.1145\/2939672.2939751"},{"key":"1780_CR22","doi-asserted-by":"crossref","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA, pp. 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"1780_CR23","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, Phoenix, Arizona, USA, pp. 1145\u20131152","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"1780_CR24","unstructured":"Feng R, Yang Y, Hu W, Wu F, Zhuang Y (2017) Representation learning for scale-free networks. In: Proceedings of the thirty-second AAAI conference on artificial intelligence , New Orleans, Louisiana, USA, pp. 282\u2013289"},{"key":"1780_CR25","doi-asserted-by":"crossref","unstructured":"Chen H, Perozzi B, Hu Y, Skiena S (2018) HARP: hierarchical representation learning for networks. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, New Orleans, Louisiana, USA, pp. 2127\u20132134","DOI":"10.1609\/aaai.v32i1.11849"},{"key":"1780_CR26","doi-asserted-by":"publisher","first-page":"25323","DOI":"10.1109\/ACCESS.2019.2900662","volume":"7","author":"W Shi","year":"2019","unstructured":"Shi W, Huang L, Wang C-D, Li J-H, Tang Y, Fu C (2019) Network embedding via community based variational autoencoder. IEEE Access 7:25323\u201325333. https:\/\/doi.org\/10.1109\/ACCESS.2019.2900662","journal-title":"IEEE Access"},{"key":"1780_CR27","doi-asserted-by":"crossref","unstructured":"Du L, Lu Z, Wang Y, Song G, Wang Y, Chen W (2018) Galaxy network embedding: a hierarchical community structure preserving approach. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI 2018, Stockholm, Sweden, pp. 2079\u20132085","DOI":"10.24963\/ijcai.2018\/287"},{"key":"1780_CR28","unstructured":"Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence, IJCAI 2015, Buenos Aires, Argentina, pp. 2111\u20132117"},{"key":"1780_CR29","doi-asserted-by":"crossref","unstructured":"Zhang D, Yin J, Zhu X, Zhang C (2016) Collective classification via discriminative matrix factorization on sparsely labeled networks. In: Proceedings of the 25th ACM international on conference on information and knowledge management, Indianapolis, Indiana, USA, pp. 1563\u20131572","DOI":"10.1145\/2983323.2983754"},{"key":"1780_CR30","unstructured":"Pan S, Wu J, Zhu X, Zhang C, Wang Y (2016) tri-party deep network representation. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, New York, USA, pp. 1895\u20131901"},{"key":"1780_CR31","doi-asserted-by":"crossref","unstructured":"Huang X, Li J, Hu X (2017) Label informed attributed network embedding. In: Proceedings of the tenth ACM international conference on web search and data mining, Cambridge, United Kingdom, pp. 731\u2013739","DOI":"10.1145\/3018661.3018667"},{"key":"1780_CR32","doi-asserted-by":"crossref","unstructured":"Huang X, Li J, Hu X (2017) Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM international conference on data mining, Houston, Texas, USA, pp. 633\u2013641","DOI":"10.1137\/1.9781611974973.71"},{"issue":"12","key":"1780_CR33","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1109\/TKDE.2018.2819980","volume":"30","author":"L Liao","year":"2018","unstructured":"Liao L, He X, Zhang H, Chua T (2018) Attributed social network embedding. IEEE Trans Knowl Data Eng 30(12):2257\u20132270. https:\/\/doi.org\/10.1109\/TKDE.2018.2819980","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1780_CR34","doi-asserted-by":"crossref","unstructured":"Gao H, Huang H (2018) Deep attributed network embedding. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, Stockholm, Sweden, pp. 3364\u20133370","DOI":"10.24963\/ijcai.2018\/467"},{"issue":"5","key":"1780_CR35","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/TNNLS.2019.2920267","volume":"31","author":"C Zheng","year":"2020","unstructured":"Zheng C, Pan L, Wu P (2020) Multimodal deep network embedding with integrated structure and attribute information. IEEE Transactions on Neural Networks and Learning Systems 31(5):1437\u20131449. https:\/\/doi.org\/10.1109\/TNNLS.2019.2920267","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1780_CR36","doi-asserted-by":"crossref","unstructured":"Jin D, Ge M, Yang L, He D, Wang L, Zhang W (2018) Integrative network embedding via deep joint reconstruction. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, Stockholm, Sweden, pp. 3407\u20133413","DOI":"10.24963\/ijcai.2018\/473"},{"issue":"12","key":"1780_CR37","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1073\/pnas.122653799","volume":"99","author":"M Girvan","year":"2001","unstructured":"Girvan M, Newman M (2001) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821\u20137826. https:\/\/doi.org\/10.1073\/pnas.122653799","journal-title":"Proc Natl Acad Sci"},{"key":"1780_CR38","doi-asserted-by":"publisher","first-page":"036104","DOI":"10.1103\/PhysRevE.74.036104","volume":"74","author":"M Newman","year":"2006","unstructured":"Newman M (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74:036104. https:\/\/doi.org\/10.1103\/PhysRevE.74.036104","journal-title":"Phys Rev E"},{"key":"1780_CR39","doi-asserted-by":"publisher","unstructured":"M. E. J. Newman, \u201cModularity and community structure in networks,\u201d Proceedings of the National Academy of Sciences, vol. 103, no. 23, pp. 8577\u20138582, 2006. Newman MEJ (2006) Modularity and community structure in networks. Proc. Natl. Acad. Sci 103(23):8577. https:\/\/doi.org\/10.1073\/pnas.0601602103","DOI":"10.1073\/pnas.0601602103"},{"key":"1780_CR40","volume-title":"Maximizing modularity is hard","author":"U Brandes","year":"2006","unstructured":"Brandes U, Delling D, Gaertler M, Goerke R, Hoefer M, Nikoloski Z, Wagner D (2006) Maximizing modularity is hard. Physics"},{"issue":"6755","key":"1780_CR41","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791. https:\/\/doi.org\/10.1038\/44565","journal-title":"Nature"},{"key":"1780_CR42","unstructured":"Akata Z, Thurau C, Bauckhage C (2011) Non-negative matrix factorization in multimodality data for segmentation and label prediction. In: proceedings of the 16th computer vision winter workshop, Mitterberg, Austria"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01780-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01780-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01780-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T05:57:06Z","timestamp":1667282226000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01780-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,6]]},"references-count":42,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["1780"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01780-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,7,6]]},"assertion":[{"value":"6 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}