{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:49:34Z","timestamp":1776080974084,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T00:00:00Z","timestamp":1566777600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T00:00:00Z","timestamp":1566777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1007\/s10618-019-00650-2","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T12:02:42Z","timestamp":1566820962000},"page":"1953-1980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Attributed network embedding via subspace discovery"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1803-5768","authenticated-orcid":false,"given":"Daokun","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jie","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Xingquan","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Chengqi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"issue":"28","key":"650_CR1","doi-asserted-by":"publisher","first-page":"11433","DOI":"10.1073\/pnas.0811511106","volume":"106","author":"G Bianconi","year":"2009","unstructured":"Bianconi G, Pin P, Marsili M (2009) Assessing the relevance of node features for network structure. Proc Natl Acad Sci 106(28):11433\u201311438","journal-title":"Proc Natl Acad Sci"},{"key":"650_CR2","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 conference on information and knowledge management. ACM, pp 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"650_CR3","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Proceedings of the 30th AAAI conference on artificial intelligence. AAAI Press, pp 1145\u20131152","DOI":"10.1609\/aaai.v30i1.10179"},{"issue":"Aug","key":"650_CR4","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9(Aug):1871\u20131874","journal-title":"J Mach Learn Res"},{"key":"650_CR5","doi-asserted-by":"crossref","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. ACM, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"650_CR6","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1109\/TKDE.2018.2846555","volume":"31","author":"T Guo","year":"2018","unstructured":"Guo T, Pan S, Zhu X, Zhang C (2018) CFOND: consensus factorization for co-clustering networked data. IEEE Trans Knowl Data Eng 31:706\u2013719","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"Feb","key":"650_CR7","first-page":"307","volume":"13","author":"MU Gutmann","year":"2012","unstructured":"Gutmann MU, Hyv\u00e4rinen A (2012) Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J Mach Learn Res 13(Feb):307\u2013361","journal-title":"J Mach Learn Res"},{"key":"650_CR8","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024\u20131034"},{"issue":"3\/4","key":"650_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.2307\/2333955","volume":"28","author":"H Hotelling","year":"1936","unstructured":"Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3\/4):321\u2013377","journal-title":"Biometrika"},{"key":"650_CR10","doi-asserted-by":"crossref","unstructured":"Huang X, Li J, Hu X (2017a) Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM international conference on data mining. SIAM, pp 633\u2013641","DOI":"10.1137\/1.9781611974973.71"},{"key":"650_CR11","doi-asserted-by":"crossref","unstructured":"Huang X, Li J, Hu X (2017b) Label informed attributed network embedding. In: Proceedings of the 10th ACM international conference on web search and data mining. ACM, pp 731\u2013739","DOI":"10.1145\/3018661.3018667"},{"key":"650_CR12","doi-asserted-by":"crossref","unstructured":"Kuang D, Ding C, Park H (2012) Symmetric nonnegative matrix factorization for graph clustering. In: Proceedings of the 2012 SIAM international conference on data mining. SIAM, pp 106\u2013117","DOI":"10.1137\/1.9781611972825.10"},{"key":"650_CR13","unstructured":"Leskovec J, Mcauley JJ (2012) Learning to discover social circles in ego networks. In: Advances in neural information processing systems, pp 539\u2013547"},{"key":"650_CR14","unstructured":"Levy O, Goldberg Y (2014) Neural word embedding as implicit matrix factorization. In: Advances in neural information processing systems, pp 2177\u20132185"},{"key":"650_CR15","unstructured":"Li AQ, Ahmed A, Ravi S, Smola AJ (2014) Reducing the sampling complexity of topic models. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 891\u2013900"},{"key":"650_CR16","doi-asserted-by":"crossref","unstructured":"Li J, Zhu J, Zhang B (2016) Discriminative deep random walk for network classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics, vol\u00a01, pp 1004\u20131013","DOI":"10.18653\/v1\/P16-1095"},{"key":"650_CR17","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 TS (2018) Attributed social network embedding. IEEE Trans Knowl Data Eng 30:2257\u20132270","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"650_CR18","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111\u20133119"},{"issue":"12","key":"650_CR19","doi-asserted-by":"publisher","first-page":"i60","DOI":"10.1093\/bioinformatics\/btu269","volume":"30","author":"N Natarajan","year":"2014","unstructured":"Natarajan N, Dhillon IS (2014) Inductive matrix completion for predicting gene-disease associations. Bioinformatics 30(12):i60\u2013i68","journal-title":"Bioinformatics"},{"issue":"3","key":"650_CR20","doi-asserted-by":"publisher","first-page":"036104","DOI":"10.1103\/PhysRevE.74.036104","volume":"74","author":"ME Newman","year":"2006","unstructured":"Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104","journal-title":"Phys Rev E"},{"key":"650_CR21","unstructured":"Pan S, Wu J, Zhu X, Zhang C, Wang Y (2016) Tri-party deep network representation. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 1895\u20131901"},{"key":"650_CR22","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"650_CR23","unstructured":"Rahimi A, Recht B (2008) Random features for large-scale kernel machines. In: Advances in neural information processing systems, pp 1177\u20131184"},{"issue":"2","key":"650_CR24","doi-asserted-by":"publisher","first-page":"240","DOI":"10.2307\/3556658","volume":"48","author":"R Reagans","year":"2003","unstructured":"Reagans R, McEvily B (2003) Network structure and knowledge transfer: the effects of cohesion and range. Adm Sci Q 48(2):240\u2013267","journal-title":"Adm Sci Q"},{"issue":"Dec","key":"650_CR25","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J (2002) Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(Dec):583\u2013617","journal-title":"J Mach Learn Res"},{"key":"650_CR26","first-page":"53","volume":"8","author":"K Subbaraj","year":"2015","unstructured":"Subbaraj K, Sundan B (2015) What happens next? Prediction of disastrous links in covert networks. Disaster Adv 8:53\u201360","journal-title":"Disaster Adv"},{"key":"650_CR27","doi-asserted-by":"crossref","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. ACM, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"issue":"Dec","key":"650_CR28","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(Dec):3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"650_CR29","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. ACM, pp 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"650_CR30","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 31st AAAI conference on artificial intelligence, pp 203\u2013209","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"650_CR31","unstructured":"Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information. In: Proceedings of the 24th international joint conference on artificial intelligence, pp 2111\u20132117"},{"key":"650_CR32","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. ACM, pp 367\u2013376","DOI":"10.1145\/3132847.3132975"},{"key":"650_CR33","doi-asserted-by":"crossref","unstructured":"Zhang D, Yin J, Zhu X, Zhang C (2016a) Collective classification via discriminative matrix factorization on sparsely labeled networks. In: Proceedings of the 25th ACM international conference on information and knowledge management. ACM, pp 1563\u20131572","DOI":"10.1145\/2983323.2983754"},{"key":"650_CR34","doi-asserted-by":"crossref","unstructured":"Zhang D, Yin J, Zhu X, Zhang C (2016b) Homophily, structure, and content augmented network representation learning. In: Proceedings of the 16th IEEE international conference on data mining. IEEE, pp 609\u2013618","DOI":"10.1109\/ICDM.2016.0072"},{"key":"650_CR35","doi-asserted-by":"crossref","unstructured":"Zhang D, Yin J, Zhu X, Zhang C (2017) User profile preserving social network embedding. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3378\u20133384","DOI":"10.24963\/ijcai.2017\/472"},{"key":"650_CR36","unstructured":"Zhang D, Yin J, Zhu X, Zhang C (2018) Network representation learning: a survey. IEEE Trans Big Data (in press)"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-019-00650-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-019-00650-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-019-00650-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T11:50:01Z","timestamp":1664193001000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-019-00650-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,26]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,11]]}},"alternative-id":["650"],"URL":"https:\/\/doi.org\/10.1007\/s10618-019-00650-2","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,26]]},"assertion":[{"value":"19 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}