Visualização de grandes redes em ambiente web
Resumo
O presente trabalho apresenta uma metodologia de visualização de grandes redes em um ambiente web através do uso de diferentes algoritmos de layout e agrupamento hierárquico, que são combinados e avaliados por diversas métricas de agrupamento. A partir da melhor combinação de layout agrupamento avaliada é gerada uma visualização de grafos no plano utilizando múltiplos níveis de agrupamento, permitindo buscas por atributos, exploração das informações nos grupos e vértices em diversos níveis simultaneamente.
Palavras-chave:
Visualização de Redes, Ambiente Web, Grandes Redes
Referências
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Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks.
Boutin, F. and Hascoet, M. (2004). Cluster validity indices for graph partitioning. In Information Visualisation, 2004. IV 2004. Proceedings. Eighth International Conference on, pages 376–381.
Brandes, U., Gaertler, M., and Wagner, D. (2003). Experiments on graph clustering algorithms. In ESA, pages 568–579.
Calínski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1–27.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (2):224–227.
Ding, C., He, X., Zha, H., Gu, M., and Simon, H. (2001). Spectral min-max cut for graph partitioning and data clustering. Lawrence Berkeley National Lab. Tech. report, 47848.
Dunny, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 4(1):95–104.
Fruchterman, T. M. and Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and experience, 21(11):1129–1164.
Heer,M. B., Ogievetsky, V., and Jeffrey (2011). D3: Data-driven documents. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis).
Hu, Y. (2005). Efficient, high-quality force-directed graph drawing. Mathematica Journal, 10(1):37–71.
Jacomy, M., Venturini, T., Heymann, S., and Bastian, M. (2014). Forceatlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE, 9(6):e98679.
King, A. D., Przulj, N., and Jurisica, I. (2004). Protein complex prediction via cost-based clustering. Bioinformatics, 20(17):3013–20.
Milligan, G.W. and Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2):159–179.
Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2):026113.
Noack, A. (2007). Energy models for graph clustering. J. Graph Algorithms Appl., 11(2):453–480.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825–2830.
Salton, G. and Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5):513–523.
Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301):236–244.
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks.
Boutin, F. and Hascoet, M. (2004). Cluster validity indices for graph partitioning. In Information Visualisation, 2004. IV 2004. Proceedings. Eighth International Conference on, pages 376–381.
Brandes, U., Gaertler, M., and Wagner, D. (2003). Experiments on graph clustering algorithms. In ESA, pages 568–579.
Calínski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1–27.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (2):224–227.
Ding, C., He, X., Zha, H., Gu, M., and Simon, H. (2001). Spectral min-max cut for graph partitioning and data clustering. Lawrence Berkeley National Lab. Tech. report, 47848.
Dunny, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 4(1):95–104.
Fruchterman, T. M. and Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and experience, 21(11):1129–1164.
Heer,M. B., Ogievetsky, V., and Jeffrey (2011). D3: Data-driven documents. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis).
Hu, Y. (2005). Efficient, high-quality force-directed graph drawing. Mathematica Journal, 10(1):37–71.
Jacomy, M., Venturini, T., Heymann, S., and Bastian, M. (2014). Forceatlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE, 9(6):e98679.
King, A. D., Przulj, N., and Jurisica, I. (2004). Protein complex prediction via cost-based clustering. Bioinformatics, 20(17):3013–20.
Milligan, G.W. and Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2):159–179.
Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2):026113.
Noack, A. (2007). Energy models for graph clustering. J. Graph Algorithms Appl., 11(2):453–480.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825–2830.
Salton, G. and Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5):513–523.
Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301):236–244.
Publicado
01/08/2015
Como Citar
NASCIMENTO, Felipe; MELO-MINARDI, Raquel.
Visualização de grandes redes em ambiente web. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 4. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. .
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2015.6797.
