{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:43Z","timestamp":1740160303709,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s13278-021-00795-3","type":"journal-article","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T09:03:32Z","timestamp":1647507812000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A framework to preserve distance-based graph properties in network embedding"],"prefix":"10.1007","volume":"12","author":[{"given":"Shweta","family":"Garg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3289-3950","authenticated-orcid":false,"given":"Ramasuri","family":"Narayanam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sambaran","family":"Bandyopadhyay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"795_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed A, Shervashidze N, Narayanamurthy S, Josifovski V, Smola AJ (2013) Distributed large-scale natural graph factorization. In: Proceedings of the 22nd international conference on world wide web. ACM, pp 37\u201348","DOI":"10.1145\/2488388.2488393"},{"key":"795_CR2","doi-asserted-by":"crossref","unstructured":"Al-Sayouri S, Gujral E, Koutra D, Papalexakis E, Lam S (2020) t-pine: tensor-based predictable and interpretable node embeddings. Soc Netwo Anal Min 10","DOI":"10.1007\/s13278-020-00649-4"},{"key":"795_CR3","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1609\/aaai.v33i01.330112","volume":"33","author":"S Bandyopadhyay","year":"2019","unstructured":"Bandyopadhyay S, Lokesh N, Murty MN (2019) Outlier aware network embedding for attributed networks. Proceedings of the AAAI conference on artificial intelligence 33:12\u201319","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"795_CR4","unstructured":"Bandyopadhyay S, Aggarwal M, Murty MN (2020a) Unsupervised graph representation by periphery and hierarchical information maximization. arXiv preprint arXiv:2006.04696"},{"key":"795_CR5","doi-asserted-by":"crossref","unstructured":"Bandyopadhyay S, Lokesh N, Vivek SV, Murty M (2020b) Outlier resistant unsupervised deep architectures for attributed network embedding. In: Proceedings of the 13th international conference on web search and data mining, pp 25\u201333","DOI":"10.1145\/3336191.3371788"},{"issue":"8","key":"795_CR6","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"795_CR7","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s13278-013-0101-4","volume":"3","author":"M Budka","year":"2013","unstructured":"Budka M, Juszczyszyn K, Musial K, Musial A (2013) Molecular model of dynamic social network based on e-mail communication. Soc Netw Anal Min 3:543\u2013563","journal-title":"Soc Netw Anal Min"},{"key":"795_CR8","doi-asserted-by":"crossref","unstructured":"Cai H, Zheng V, Chang KC (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"795_CR9","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, pp 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"795_CR10","doi-asserted-by":"crossref","unstructured":"Derr T, Aggarwal CC, Tang J (2018) Signed network modeling based on structural balance theory. In: 27th ACM international conference on information and knowledge management (CIKM), pp 557\u2013566","DOI":"10.1145\/3269206.3271746"},{"key":"795_CR11","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"volume-title":"Networks, crowds, and markets: reasoning about a highly connected world","year":"2010","key":"795_CR12","unstructured":"Easley D, Kleinberg J (eds) (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge"},{"key":"795_CR13","doi-asserted-by":"crossref","unstructured":"Gallagher B, Eliassi-Rad T (2008) Leveraging label-independent features for classification in sparsely labeled networks: an empirical study. In: International workshop on social network mining and analysis. Springer, pp 1\u201319","DOI":"10.1007\/978-3-642-14929-0_1"},{"key":"795_CR14","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","volume":"151","author":"P Goyal","year":"2018","unstructured":"Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst 151:78\u201394","journal-title":"Knowl Based Syst"},{"key":"795_CR15","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":"795_CR16","doi-asserted-by":"crossref","unstructured":"G\u00fcnd\u00fcz-\u00d6\u011f\u00fcd\u00fcc\u00fc \u015e, ima Etaner-Uyar A\u015e (2014) Social networks: analysis and case studies. Lecture notes in social Networks book series (LNSN)","DOI":"10.1007\/978-3-7091-1797-2"},{"key":"795_CR17","unstructured":"Hamilton W, Ying R, Leskovec J (2017a) Representation learning on graphs: methods and applications. IEEE Data Eng Bull"},{"key":"795_CR18","unstructured":"Hamilton W, Ying Z, Leskovec J (2017b) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1025\u20131035"},{"key":"795_CR19","doi-asserted-by":"crossref","unstructured":"Hewapathirana I, Lee D, Moltchanova E, McLeod J (2020) Change detection in noisy dynamic networks: a spectral embedding approach. Soc Netw Anal Min 10","DOI":"10.1007\/s13278-020-0625-3"},{"key":"795_CR20","doi-asserted-by":"crossref","unstructured":"Huang X, Li J, Hu X (2017) 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":"795_CR21","doi-asserted-by":"crossref","unstructured":"Kawash J (2014) Online social media analysis and visualization. Lecture notes in social networks book series (LNSN)","DOI":"10.1007\/978-3-319-13590-8"},{"key":"795_CR22","unstructured":"Kipf TN, Welling M (2016a) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"795_CR23","unstructured":"Kipf TN, Welling M (2016b) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"795_CR24","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":"795_CR25","doi-asserted-by":"crossref","unstructured":"Li X, Du N, Li H, Li K, Gao J, Zhang A (2014) A deep learning approach to link prediction in dynamic networks. In: Proceedings of the SIAM international conference on data mining (SDM), pp 289\u2013297","DOI":"10.1137\/1.9781611973440.33"},{"key":"795_CR26","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"},{"key":"795_CR27","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1006\/game.1996.0044","volume":"14","author":"D Monderer","year":"1996","unstructured":"Monderer D, Shapley L (1996) Potential games. Games Econ Behav 14:124\u2013143","journal-title":"Games Econ Behav"},{"key":"795_CR28","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199206650.001.0001","volume-title":"Networks: an antroduction","author":"M Newman","year":"2010","unstructured":"Newman M (2010) Networks: an antroduction. Oxford University Press, Oxford"},{"key":"795_CR29","unstructured":"Niepert M, Ahmed M, Kutzkov K (2016) Learning convolutional neural networks for graphs. In: International conference on machine learning, pp 2014\u20132023"},{"key":"795_CR30","doi-asserted-by":"crossref","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: 22nd ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 1105\u201311147","DOI":"10.1145\/2939672.2939751"},{"key":"795_CR31","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":"795_CR32","doi-asserted-by":"crossref","unstructured":"Ribeiro LF, Saverese PH, Figueiredo DR (2017) struc2vec: Learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 385\u2013394","DOI":"10.1145\/3097983.3098061"},{"key":"795_CR33","volume-title":"Diffusion of innovations","author":"E Rogers","year":"1995","unstructured":"Rogers E (1995) Diffusion of innovations. Free Press, New York"},{"issue":"3","key":"795_CR34","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93\u201393","journal-title":"AI Mag"},{"key":"795_CR35","doi-asserted-by":"crossref","unstructured":"Skillicorn D, Zheng Q, Morselli C (2014) Modeling dynamic social networks using spectral embedding. Soc Netw Anal Min 4","DOI":"10.1007\/s13278-014-0182-8"},{"key":"795_CR36","doi-asserted-by":"crossref","unstructured":"Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) Biogrid: a general repository for interaction datasets. Nucleic Acids Res34(suppl\\_1), D535\u2013D539","DOI":"10.1093\/nar\/gkj109"},{"key":"795_CR37","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, pp 1067\u20131077. International world wide web conferences steering committee","DOI":"10.1145\/2736277.2741093"},{"key":"795_CR38","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International Conference on learning representations"},{"key":"795_CR39","unstructured":"Veli\u010dkovi\u0107 P, Fedus W, Hamilton WL, Li\u00f2 P, Bengio Y, Hjelm RD (2018) Deep graph infomax. In: International conference on learning representations"},{"key":"795_CR40","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":"795_CR41","doi-asserted-by":"crossref","unstructured":"Wang S, Tang J, Aggarwal CC, Chang Y, Liu H (2017) Signed network embedding in social media. In: SIAM International conference on data mining (SDM), pp 327\u2013335","DOI":"10.1137\/1.9781611974973.37"},{"key":"795_CR42","doi-asserted-by":"crossref","unstructured":"Wang H, Wang J, Wang J, Zhao M, Zhang W, Zhang F, Xie X, Guo M (2018) Graphgan: graph representation learning with generative adversarial nets. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, New Orleans, Louisiana, USA, February 2\u20137, 2018. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16611","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"795_CR43","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2019) A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596"},{"issue":"4","key":"795_CR44","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1086\/jar.33.4.3629752","volume":"33","author":"WW Zachary","year":"1977","unstructured":"Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452\u2013473","journal-title":"J Anthropol Res"},{"key":"795_CR45","unstructured":"Zafarani R, Liu H (2009) Social computing data repository at ASU. http:\/\/socialcomputing.asu.edu"},{"key":"795_CR46","doi-asserted-by":"crossref","unstructured":"Zhai S, Zhang Z.M (2015) Dropout training of matrix factorization and autoencoder for link prediction in sparse graphs. In: Proceedings of the SIAM international conference on data mining (SDM), pp 451\u2013459","DOI":"10.1137\/1.9781611974010.51"},{"key":"795_CR47","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. AAAI Press, pp 3378\u20133384","DOI":"10.24963\/ijcai.2017\/472"},{"key":"795_CR48","doi-asserted-by":"crossref","unstructured":"Zheng Q, Skillicorn D (2016) Spectral embedding of directed networks. Soc Netw Anal Min 6","DOI":"10.1007\/s13278-016-0387-0"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-021-00795-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-021-00795-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-021-00795-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T13:53:45Z","timestamp":1672667625000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-021-00795-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,17]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["795"],"URL":"https:\/\/doi.org\/10.1007\/s13278-021-00795-3","relation":{},"ISSN":["1869-5450","1869-5469"],"issn-type":[{"type":"print","value":"1869-5450"},{"type":"electronic","value":"1869-5469"}],"subject":[],"published":{"date-parts":[[2022,3,17]]},"assertion":[{"value":"12 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"44"}}