Visualization of large networks in web environment

• Felipe Nascimento Federal University of Minas Gerais
• Raquel Melo-Minardi Federal University of Minas Gerais

DOI: https://doi.org/10.5753/brasnam.2015.6797

Abstract

The present work presents a methodology for visualizing large networks in a web environment through the use of different layout algorithms and hierarchical grouping, which are combined and evaluated by several grouping metrics. From the best combination of grouped layout evaluation is generated a visualization of graphs in the plane using multiple levels of grouping, allowing searches by attributes, exploration of information in groups and vertices at several levels simultaneously.

Keywords: Network Visualization, Web Environment, Large Networks

References

Arthur, D. and Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027–1035.

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.