{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:17:03Z","timestamp":1740107823922,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T00:00:00Z","timestamp":1574208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T00:00:00Z","timestamp":1574208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1333109"],"award-info":[{"award-number":["U1333109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"a fund from the Hong Kong Polytechnic University, Department of Industrial and Systems Engineering","award":["H-ZG3"],"award-info":[{"award-number":["H-ZG3"]}]},{"name":"Fundamental Research Funds for the Central Universities of Civil Aviation University of China","award":["3122018C020","3122018C021"],"award-info":[{"award-number":["3122018C020","3122018C021"]}]},{"name":"Scientic Research Foundation of Civil Aviation University of China","award":["600\/600001050115","600\/600001050117"],"award-info":[{"award-number":["600\/600001050115","600\/600001050117"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00500-019-04451-z","type":"journal-article","created":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T10:03:02Z","timestamp":1574244182000},"page":"8223-8231","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A network representation method based on edge information extraction"],"prefix":"10.1007","volume":"24","author":[{"given":"Wei","family":"Fan","sequence":"first","affiliation":[]},{"given":"Hui Min","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Huang","sequence":"additional","affiliation":[]},{"given":"W. H.","family":"Ip","sequence":"additional","affiliation":[]},{"given":"Kai Leung","family":"Yung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,20]]},"reference":[{"key":"4451_CR1","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems (NIPS), pp 585\u2013591","DOI":"10.7551\/mitpress\/1120.003.0080"},{"key":"4451_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 2015 ACM on conference on information and knowledge management (CIKM), ACM, pp 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"4451_CR3","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 (KDD), ACM, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"issue":"7\u20138","key":"4451_CR4","first-page":"1023","volume":"13","author":"DT Hoang","year":"2018","unstructured":"Hoang DT, Nguyen NT, Tran VC, Hwang D (2018) Research collaboration model in academic social networks. Enterp Inf Syst 13(7\u20138):1023\u20131045","journal-title":"Enterp Inf Syst"},{"key":"4451_CR5","doi-asserted-by":"crossref","unstructured":"Hong R, He Y, Wu L, Ge Y, Wu X (2019) Deep attributed network embedding by preserving structure and attribute information. IEEE Trans Syst Man Cybern Syst pp 1\u201312","DOI":"10.1109\/TSMC.2019.2897152"},{"issue":"7","key":"4451_CR6","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1080\/17517575.2013.804586","volume":"9","author":"W Hu","year":"2015","unstructured":"Hu W, Gong Z, LH U, Guo J (2015) Identifying influential user communities on the social network. Enterp Inf Syst 9(7):709\u2013724","journal-title":"Enterp Inf Syst"},{"issue":"2","key":"4451_CR7","doi-asserted-by":"publisher","first-page":"026120","DOI":"10.1103\/PhysRevE.73.026120","volume":"73","author":"EA Leicht","year":"2006","unstructured":"Leicht EA, Holme P, Newman ME (2006) Vertex similarity in networks. Phys Rev E 73(2):026120","journal-title":"Phys Rev E"},{"key":"4451_CR8","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.physa.2014.05.056","volume":"410","author":"Y Li","year":"2014","unstructured":"Li Y, Luo P, Wu C (2014) Information loss method to measure node similarity in networks. Phys A 410:439\u2013449","journal-title":"Phys A"},{"issue":"12","key":"4451_CR9","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(12):2257\u20132270","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4451_CR10","unstructured":"Luo D, Nie F, Huang H, Ding CH (2011) Cauchy graph embedding. In: Proceedings of the 28th international conference on machine learning (ICML), ACM, pp 553\u2013560"},{"key":"4451_CR11","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"4451_CR12","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems (NIPS), pp 3111\u20133119"},{"key":"4451_CR13","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 (KDD), ACM, pp 1105\u20131114","DOI":"10.1145\/2939672.2939751"},{"key":"4451_CR14","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 (KDD), ACM, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"4451_CR15","doi-asserted-by":"crossref","unstructured":"Qu M, Tang J, Shang J, Ren X, Zhang M, Han J (2017) An attention-based collaboration framework for multi-view network representation learning. In: Proceedings of the 2017 ACM on conference on information and knowledge management (CIKM), ACM, pp 1767\u20131776","DOI":"10.1145\/3132847.3133021"},{"issue":"5586","key":"4451_CR16","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1126\/science.1073374","volume":"297","author":"E Ravasz","year":"2002","unstructured":"Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barab\u00e1si AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551\u20131555","journal-title":"Science"},{"issue":"5500","key":"4451_CR17","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"3929","key":"4451_CR18","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1126\/science.168.3929.335","volume":"168","author":"G Salton","year":"1970","unstructured":"Salton G (1970) Automatic text analysis. Science 168(3929):335\u2013343","journal-title":"Science"},{"key":"4451_CR19","doi-asserted-by":"crossref","unstructured":"Shi Y, Zhu Q, Guo F, Zhang C, Han J (2018) Easing embedding learning by comprehensive transcription of heterogeneous information networks. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), ACM, pp 2190\u20132199","DOI":"10.1145\/3219819.3220006"},{"key":"4451_CR20","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang S, Hsieh TY, Tang X, Honavar V (2019) Megan: a generative adversarial network for multi-view network embedding. In: Proceedings of the 28th international joint conference on artificial intelligence (IJCAI), AAAI Press, pp 3527\u20133533","DOI":"10.24963\/ijcai.2019\/489"},{"key":"4451_CR21","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 (WWW), ACM, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"4451_CR22","unstructured":"Tu C, Zhang W, Liu Z, Sun M, et\u00a0al. (2016) Max-margin deepwalk: discriminative learning of network representation. In: Proceedings of the 25th international joint conference on artificial intelligence (IJCAI), AAAI Press, pp 3889\u20133895"},{"key":"4451_CR23","doi-asserted-by":"crossref","unstructured":"Tu C, Liu H, Liu Z, Sun M (2017a) Cane: context-aware network embedding for relation modeling. In: Proceedings of the 55th annual meeting of the association for computational linguistics (ACL), pp 1722\u20131731","DOI":"10.18653\/v1\/P17-1158"},{"key":"4451_CR24","doi-asserted-by":"crossref","unstructured":"Tu C, Zhang Z, Liu Z, Sun M (2017b) Transnet: translation-based network representation learning for social relation extraction. In: Proceedings of the 26th international joint conference on artificial intelligence (IJCAI), AAAI Press, pp 2864\u20132870","DOI":"10.24963\/ijcai.2017\/399"},{"issue":"1","key":"4451_CR25","first-page":"3221","volume":"15","author":"L Van Der Maaten","year":"2014","unstructured":"Van Der Maaten L (2014) Accelerating t-sne using tree-based algorithms. J Mach Learn Res 15(1):3221\u20133245","journal-title":"J Mach Learn Res"},{"key":"4451_CR26","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 (KDD), ACM, pp 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"4451_CR27","doi-asserted-by":"crossref","unstructured":"Wang S, Hu L, Cao L (2017a) Perceiving the next choice with comprehensive transaction embeddings for online recommendation. In: Joint European conference on machine learning and knowledge discovery in databases (ECML), Springer, pp 285\u2013302","DOI":"10.1007\/978-3-319-71246-8_18"},{"key":"4451_CR28","doi-asserted-by":"crossref","unstructured":"Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017b) Community preserving network embedding. In: Proceddings of the 31st AAAI conference on artificial intelligence (AAAI), pp 203\u2013209","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"4451_CR29","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1109\/LCOMM.2017.2705695","volume":"21","author":"Y Wang","year":"2017","unstructured":"Wang Y, Ding M, Chen Z, Luo L (2017c) Caching placement with recommendation systems for cache-enabled mobile social networks. IEEE Commun Lett 21:2266\u20132269","journal-title":"IEEE Commun Lett"},{"key":"4451_CR30","unstructured":"Yang C, Liu Z, Zhao D, Sun M, Chang E (2015) Network representation learning with rich text information. In: Proceedings of the 24th international joint conference on artificial intelligence (IJCAI), AAAI Press, pp 2111\u20132117"},{"key":"4451_CR31","doi-asserted-by":"crossref","unstructured":"Yang H, Pan S, Zhang P, Chen L, Lian D, Zhang C (2018) Binarized attributed network embedding. In: Proceedings of the 2018 IEEE international conference on data mining (ICDM), IEEE, pp 1476\u20131481","DOI":"10.1109\/ICDM.2018.8626170"},{"key":"4451_CR32","doi-asserted-by":"crossref","unstructured":"Yang H, Pan S, Chen L, Zhou C, Zhang P (2019) Low-bit quantization for attributed network representation learning. In: Proceedings of the 28th international joint conference on artificial intelligence (IJCAI), AAAI Press, pp 4047\u20134053","DOI":"10.24963\/ijcai.2019\/562"},{"key":"4451_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1847979017712629","volume":"9","author":"S Zedan","year":"2017","unstructured":"Zedan S, Miller W (2017) Using social network analysis to identify stakeholders\u2019 influence on energy efficiency of housing. Int J Eng Bus Manag 9:1\u20132","journal-title":"Int J Eng Bus Manag"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-04451-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-019-04451-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-04451-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T11:33:42Z","timestamp":1722080022000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-019-04451-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,20]]},"references-count":33,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4451"],"URL":"https:\/\/doi.org\/10.1007\/s00500-019-04451-z","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2019,11,20]]},"assertion":[{"value":"20 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}