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Feature representation for social circles detection using MAC

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Feature representation for social circles detection using MAC

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dc.contributor.author Alonso-Nanclares, Jesús Alberto es_ES
dc.contributor.author Paredes Palacios, Roberto es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2017-06-09T09:26:20Z
dc.date.available 2017-06-09T09:26:20Z
dc.date.issued 2016-02
dc.identifier.issn 0941-0643
dc.identifier.uri http://hdl.handle.net/10251/82638
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2222-y es_ES
dc.description.abstract Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on two available labelled Facebook datasets and compare our results with several different baselines. In addition, we provide some insights of the evaluation metrics most commonly used in the literature. es_ES
dc.description.sponsorship This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), funded by the US Army Research Office (ARO). The work of the first author is financed by Grant FPU14/03483, from the Spanish Ministry of Education, Culture and Sport. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Neural Computing and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Social circles detection es_ES
dc.subject Community detection es_ES
dc.subject Feature representation es_ES
dc.subject Multi-assignment clustering es_ES
dc.subject Evaluation metrics es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Feature representation for social circles detection using MAC es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00521-016-2222-y
dc.relation.projectID info:eu-repo/grantAgreement/ARO//W911NF-14-1-0254/US/Empirical Evaluation of Different Feature Representations for Social Circles Detection/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU14%2F03483/ES/FPU14%2F03483/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Alonso-Nanclares, JA.; Paredes Palacios, R.; Rosso, P. (2016). Feature representation for social circles detection using MAC. Neural Computing and Applications. 1-8. https://doi.org/10.1007/s00521-016-2222-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://link.springer.com/article/10.1007/s00521-016-2222-y es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 8 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 324372 es_ES
dc.contributor.funder Army Research Office, EEUU es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES
dc.description.references Alonso J, Paredes R, Rosso P (2015) Empirical evaluation of different feature representations for social circles detection. In: Pattern recognition and image analysis, lecture notes in computer science, vol. 9117, pp 31–38. Springer, Berlin. doi: 10.1007/978-3-319-19390-8_4 es_ES
dc.description.references Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theor Exp 2008:P10, 008 es_ES
dc.description.references Brandes U, Delling D, Gaertler M, Gaerke R, Hoefer M, Nikoloski Z, Wagner D (2006) On modularity-NP-completeness and beyond. Technical Report. 2006–19, ITI Wagner, Faculty of Informatics, Universität Karlsruhe (TH), Germany es_ES
dc.description.references Buhmann J, Kuhnel H (1993) Vector quantization with complexity costs. IEEE Trans Inf Theory 39(4):1133–1145 es_ES
dc.description.references Chen Y, Lin C (2006) Combining SVMs with various feature selection strategies. In: Feature extraction, pp 315–324 es_ES
dc.description.references Dey K, Bandyopadhyay S (2013) An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International conference on natural languages es_ES
dc.description.references Donath WE, Hoffman AJ (1973) Lower bounds for the partitioning of graphs. IBM J Res Dev 17(5):420–425 es_ES
dc.description.references Everitt BS, Hand DJ (1981) Finite mixture distributions. Chapman and Hall, London es_ES
dc.description.references Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174 es_ES
dc.description.references Frank M, Streich AP, Basin D, Buhmann JM (2012) Multi-assignment clustering for Boolean data. J Mach Learn Res 13(1):459–489 es_ES
dc.description.references Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826 es_ES
dc.description.references Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, Berlin es_ES
dc.description.references Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bulletin de la Socit Vaudoise des Sciences Naturelles 44(163):223–270 es_ES
dc.description.references Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circles es_ES
dc.description.references Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307 es_ES
dc.description.references Leskovec J, Krevl A (2014) SNAP datasets: stanford large network dataset collection. http://snap.stanford.edu/data es_ES
dc.description.references Leskovec J, Mcauley J (2012) Learning to discover social circles in ego networks. In: Pereira F, Burges C, Bottou L, Weinberger K (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., Red Hook, pp 539–547 es_ES
dc.description.references Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137 es_ES
dc.description.references MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of fifth Berkeley symposium on Mathematical Statistics and Probability, vol 1, pp 281–297 es_ES
dc.description.references McAuley J, Leskovec J (2014) Discovering social circles in ego networks. ACM Trans Knowl Discov Data 8(1):4 es_ES
dc.description.references Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Ind Appl Math 5(1):32–38 es_ES
dc.description.references Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582 es_ES
dc.description.references Newman ME, Girvan M (2014) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2):026,113 es_ES
dc.description.references Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818 es_ES
dc.description.references Pathak N, DeLong C, Banerjee A, Erickson K (2008) Social topic models for community extraction. In: The 2nd SNA-KDD workshop es_ES
dc.description.references Porter MA, Onnela JP, Mucha PJ (2009) Communities in networks. Not Am Math Soc 56(9):1082–1097 es_ES
dc.description.references Rose K, Gurewitz E, Fox GC (1992) Vector quantization by deterministic annealing. IEEE Trans Inf Theory 38(4):1249–1257 es_ES
dc.description.references Sachan M, Contractor D, Faruqie TA, Subramaniam LV (2012) Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st international conference on World Wide Web, pp 331–340 es_ES
dc.description.references Streich AP, Frank M, Basin D, Buhmann JM (2009) Multi-assignment clustering for boolean data. In: Proceedings of the 26th annual international conference on machine learning, pp 969–976 es_ES
dc.description.references Suaris PR, Kedem G (1988) An algorithm for quadrisection and its applications to standard cell placement. IEEE Trans Circuits Syst 35(3):294–303 es_ES
dc.description.references Vaidya J, Atluri V, Guo Q (2007) The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM symposium on access control models and technologies, pp 175–184 es_ES
dc.description.references Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes. In: IEEE 13th international conference on data mining (ICDM), pp 1151–1156. IEEE es_ES
dc.description.references Zhou D, Councill I, Zha H, Giles CL (2007) Discovering temporal communities from social network documents. In: Seventh IEEE international conference on data mining, pp 745–750 es_ES


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