A divide-link algorithm based on fuzzy similarity for clustering networks
| dc.book.title | Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on | |
| dc.contributor.author | Gómez González, Daniel | |
| dc.contributor.author | Montero De Juan, Francisco Javier | |
| dc.contributor.author | Yáñez Gestoso, Francisco Javier | |
| dc.date.accessioned | 2023-06-20T05:46:30Z | |
| dc.date.available | 2023-06-20T05:46:30Z | |
| dc.date.issued | 2011 | |
| dc.description.abstract | In this paper we present an efficient hierarchical clustering algorithm for relational data, being those relations modeled by a graph. The hierarchical clustering approach proposed in this paper is based on divisive and link criteria, to break the graph and join the nodes at different stages. We then apply this approach to a community detection problems based on the well-known edge line betweenness measure as the divisive criterium and a fuzzy similarity relation as the link criterium. We present also some computational results in some well-known examples like the Karate Zachary club-network, the Dolphins network, Les Miserables network and the Authors centrality network, comparing these results to some standard methodologies for hierarchical clustering problem, both for binary and valued graphs. | en |
| dc.description.department | Depto. de Estadística e Investigación Operativa | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/28646 | |
| dc.identifier.doi | 10.1109/ISDA.2011.6121830 | |
| dc.identifier.isbn | 978-1-4577-1676-8 | |
| dc.identifier.officialurl | https//doi.org/10.1109/ISDA.2011.6121830 | |
| dc.identifier.relatedurl | http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6121830&abstractAccess=no&userType=inst | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/45540 | |
| dc.language.iso | eng | |
| dc.page.final | 1252 | |
| dc.page.initial | 1247 | |
| dc.page.total | 1402 | |
| dc.publisher | IEEE | |
| dc.relation.ispartofseries | Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on | |
| dc.rights.accessRights | restricted access | |
| dc.subject.cdu | 519.8 | |
| dc.subject.keyword | Graph Theory | |
| dc.subject.keyword | Community detection | |
| dc.subject.keyword | Fuzzy Similarity | |
| dc.subject.ucm | Investigación operativa (Matemáticas) | |
| dc.subject.unesco | 1207 Investigación Operativa | |
| dc.title | A divide-link algorithm based on fuzzy similarity for clustering networks | en |
| dc.type | book part | |
| dcterms.references | J. M. Anthonisse, The rush in a directed graph, Technical Report, Stichting Mathematisch Centrum, 2e Boerhaavestraat 49 Amsterdam, The Netherlands. (1971) R.K. Ahuja, T. L. Magnanti, and J. B. Orlin, 1993, Net- work Flows: Theory, Algorithms, and Applications (Prentice Hall, Englewood Cliks, USA). J. Duch and A. Arenas. Community identification using extremal optimization. Phys. Rev. E 72 027104 (2005) A. Arenas, A. Fernandez and S. Gomez, Analysis of the structure of complex networks at different resolution levels. New J. Phys. 10(5), 053039 (2008). E. Estrada, and N. Hatano, Phys. Rev. E 77(3), 036111 (2008). E. Estrada, and N. Hatano, 2009, Appl. Math. Comput. 214,500. F. Fouss, and J.-M. Renders, Random-walk computation of similarities between nodes of a graph, with application to collaborative recommendation. IEEE Trans. on Knowl. and Data Eng. 19(3), 355-369 (2007). S. Fortunato, Community detection in graphs. Physics Reports 486, 75-174 (2010). L. Freeman, Centrality in networks: I. conceptual clarification. Social Networks, 1, 215239 (1979) M. Girvan and M.E.J. Newman, Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the USA 99, 7821-7826 (2002). D. Gómez and J. Montero, Fuzzy sets in remote sensing classification. Soft Computing, 12, 243-249 (2008). D. Gómez, J. Montero and G. Biging, Improvements to remote sensing using fuzzy classification, graphs and accuracy statistics. Pure and Applied Geophysics, 165, 1555-1575 (2008). D. Gómez, J. Montero and G. Biging, Accuracy statistics for judging soft classification. International Journal of Remote Sensing, 29(3), 693-709 (2008). D. Gómez, J. Montero and J. Yánez, A coloring algorithm for image classification. Information Sciences, 176, 3645-3657 (2006). D. Gómez, J. Montero, J. Yáñez and C. Poidomani, A graph coloring algorithm approach for image segmentation. Omega 35, 173-183 (2007). S. Gregory. Fuzzy overlapping communities in networks. Journal of Statistical Mechanics: Theory and Experiment. DOI: 10.1088/1742-5468/2011/02/ P02017. (2011) J. Montero, D. Gómez and H. Bustince, On the relevance of some families of fuzzy sets. Fuzzy Sets and Systems, 158, 2429-2442 (2007). J. Montero, V. López and D. Gómez, The role of fuzziness in decision making. Studies in Fuzziness and Soft Computing, 215, 337-349 (2007). J. Montero and L. Martinez, Upgrading ideas about the concept of soft computing International Journal of Computational Intelligence Systems, 3, 144-147 (2010). M.E.J. Newman, Fast algórithm for detecting community structure in networks. Physical Review E, 69, 066133 (2004). M.E.J. Newman, Analysis of weighted networks. Physical Review E, 70, 056131 (2004). M.E.J. Newman and M. Girvan, Finding and evaluating community structure in networks. Physical Reviews, 69, 026113. M.E.J. Newman, A measure of betweenness centrality based on random walks. Social Networks, 27 (1), 3954. (2005). M. Saerens, F. Fouss, L. Yen, and P. Dupont, 2004, in Proc. Eur. Conf. on Machine Learning, URL citeseer.ist.psu. edu/saerens04principal.html. F. Tavares-Pereira, J.R. Figueira, V. Mousseau and B. Roy, Multiple criteria districting problems: The public transportation network pricing system of the Paris region. Annals of Operations Research, 154, 69-92 (2007). P.G. Sun, L. Gao, S. S. Han, Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks. Information Sciences, 181, 10601071 (2011). F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, V. and D. Parisi, Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America 101, 26582663. (2004) S. Wasserman and K. Faust. Social Networks Analysis (Cambridge University Press, 1994). J. Yáñez, S. Muñoz and J. Montero, Graph coloring inconsistencies in image segmentation. Computer Engineering and Information Sciences, 1, 435-440 (2008). L. Yen, F. Fouss, C. Decaestecker, P. Francq, and M. Saerens, 2007, in PAKDD, pp. 1037-1045. T. Zhou, J. Liu, and B.Wang, Notes on the calculation of node betweenness. Chinese Physics Letters, 23, 2327-2329 (2006). S. Yue, J.S. Wang, T. Wu and H. Wang, A new separation measure for improving the effectiveness of validity indices. Information Sciences, 180(5), 748-764 (2010). N. Yuruk, M. Mete and T. Schweiger, A divisive hierarchical structural clustering algorithm for networks. Proceedings of the 7th IEEE International Conference on Data Mining Workshops, 441-448 (2007). | |
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