NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks

Jaya Sreevalsan-Nair, Shivam Agarwal

2017

Abstract

While there are several visualizations of the small world networks (SWN), how does one find an appropriate set of visualizations and data analytic processes in a data science workflow? Hierarchical communities in SWN aid in managing and understanding the complex network better. To enable a visual analytics workflow to probe and uncover hierarchical communities, we propose to use both the network data and metadata (e.g. node and link attributes). Hence, we propose to use the network topology and node-similarity graph using metadata, for knowledge discovery. For the construction of a four-level hierarchy, we detect communities on both the network and the similarity graph, by using specific community detection at specific hierarchical level. We enable the flexibility of finding non-overlapping or overlapping communities, as leaf nodes, by using spectral clustering. We propose NodeTrix-CommunityHierarchy (NTCH), a set of visual analytic techniques for hierarchy construction, visual exploration and quantitative analysis of community detection results. We extend NodeTrix-Multiplex framework (Agarwal et al., 2017), which is for visual analytics of multilayer SWN, to probe hierarchical communities. We propose novel visualizations of overlapping and non-overlapping communities, which are integrated into the framework. We show preliminary results of our case-study of using NTCH on co-authorship networks.

References

  1. Agarwal, S., Tomar, A., and Sreevalsan-Nair, J. (2017). NodeTrix-Multiplex: Visual Analytics of Multiplex Small World Networks, pages 579-591. Springer International Publishing, Cham.
  2. Bastian, M., Heymann, S., Jacomy, M., et al. (2009). Gephi: an open source software for exploring and manipulating networks. ICWSM, 8:361-362.
  3. Bennett, L., Kittas, A., Muirhead, G., Papageorgiou, L. G., and Tsoka, S. (2015). Detection of composite communities in multiplex biological networks. Scientific reports, 5.
  4. Bezdek, J. C., Hathaway, R. J., and Huband, J. M. (2007). Visual assessment of clustering tendency for rectangular dissimilarity matrices. Fuzzy Systems, IEEE Transactions on, 15(5):890-903.
  5. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
  6. Coscia, M., Rossetti, G., Giannotti, F., and Pedreschi, D. (2014). Uncovering hierarchical and overlapping communities with a local-first approach. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(1):6.
  7. Davidson, S. B. and Freire, J. (2008). Provenance and scientific workflows: challenges and opportunities. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 1345-1350. ACM.
  8. De Domenico, M., Lancichinetti, A., Arenas, A., and Rosvall, M. (2015). Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Physical Review X, 5(1):011027.
  9. Dunbar, R. (1998). Grooming, gossip, and the evolution of language. Harvard University Press.
  10. Dunn, J. C. (1973). A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters.
  11. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3):75-174.
  12. Fortunato, S. and Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1):36-41.
  13. Ghoniem, M., Fekete, J.-D., and Castagliola, P. (2004). A comparison of the readability of graphs using nodelink and matrix-based representations. In Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on, pages 17-24. Ieee.
  14. Guo, P. (2013). Data science workflow: Overview and challenges. Communications of the ACM.
  15. Guo, P. J. (2012). Software tools to facilitate research programming. PhD thesis, Stanford University.
  16. Havens, T. C., Bezdek, J. C., Leckie, C., Ramamohanarao, K., and Palaniswami, M. (2013). A soft modularity function for detecting fuzzy communities in social networks. Fuzzy Systems, IEEE Transactions on, 21(6):1170-1175.
  17. Henry, N., Bezerianos, A., and Fekete, J.-D. (2008). Improving the readability of clustered social networks using node duplication. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1317-1324.
  18. Henry, N., Fekete, J.-D., and McGuffin, M. J. (2007). Nodetrix: a hybrid visualization of social networks. Visualization and Computer Graphics, IEEE Transactions on, 13(6):1302-1309.
  19. Huang, J., Sun, H., Han, J., Deng, H., Sun, Y., and Liu, Y. (2010). Shrink: a structural clustering algorithm for detecting hierarchical communities in networks. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 219-228. ACM.
  20. Huberman, B. A. and Adamic, L. A. (2004). Information dynamics in the networked world. In Complex networks, pages 371-398. Springer.
  21. Isenberg, P., Heimerl, F., Koch, S., Isenberg, T., Xu, P., Stolper, C., Sedlmair, M., Chen, J., Möller, T., and Stasko, J. (2015). Visualization publication dataset. Dataset: http://vispubdata.org/.
  22. Javed, W. and Elmqvist, N. (2012). Exploring the design space of composite visualization. In Visualization Symposium (PacificVis), 2012 IEEE Pacific , pages 1- 8. IEEE.
  23. Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., and Porter, M. A. (2014). Multilayer networks. Journal of complex networks, 2(3):203-271.
  24. Lancichinetti, A., Fortunato, S., and Kertész, J. (2009). Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 11(3):033015.
  25. Leskovec, J., Lang, K. J., Dasgupta, A., and Mahoney, M. W. (2009). Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics, 6(1):29- 123.
  26. Liiv, I. (2010). Seriation and matrix reordering methods: An historical overview. Statistical analysis and data mining, 3(2):70-91.
  27. Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., and Onnela, J.-P. (2010). Community structure in timedependent, multiscale, and multiplex networks. science, 328(5980):876-878.
  28. Narasimhamurthy, A., Greene, D., Hurley, N., and Cunningham, P. (2010). Partitioning large networks without breaking communities. Knowledge and information systems, 25(2):345-369.
  29. Newman, M. E. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical review E, 74(3):036104.
  30. Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2):026113.
  31. Ng, A. Y., Jordan, M. I., Weiss, Y., et al. (2002). On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2:849-856.
  32. Pal, N. R. and Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. Fuzzy Systems, IEEE Transactions on, 3(3):370-379.
  33. Parveen, S. and Sreevalsan-Nair, J. (2013). Visualization of small world networks using similarity matrices. In Big Data Analytics, pages 151-170. Springer.
  34. Perer, A. and Shneiderman, B. (2006). Balancing systematic and flexible exploration of social networks. IEEE Transactions on Visualization and Computer Graphics, 12(5):693-700.
  35. Renoust, B., Melanc¸on, G., and Munzner, T. (2015). Detangler: Visual analytics for multiplex networks. In Computer Graphics Forum, volume 34, pages 321- 330. Wiley Online Library.
  36. Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., and Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems (TOIS), 28(1):4.
  37. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53-65.
  38. Rufiange, S., McGuffin, M. J., and Fuhrman, C. P. (2012). Treematrix: A hybrid visualization of compound graphs. In Computer Graphics Forum, volume 31, pages 89-101. Wiley Online Library.
  39. Shi, L., Cao, N., Liu, S., Qian, W., Tan, L., Wang, G., Sun, J., and Lin, C.-Y. (2009). Himap: Adaptive visualization of large-scale online social networks. In Visualization Symposium, 2009. PacificVis' 09. IEEE Pacific , pages 41-48. IEEE.
  40. Strehl, A. and Ghosh, J. (2003). Relationship-based clustering and visualization for high-dimensional data mining. INFORMS Journal on Computing, 15(2):208- 230.
  41. van den Elzen, S. and van Wijk, J. J. (2014). Multivariate network exploration and presentation: From detail to overview via selections and aggregations. Visualization and Computer Graphics, IEEE Transactions on, 20(12):2310-2319.
  42. Vehlow, C., Beck, F., and Weiskopf, D. (2015). The state of the art in visualizing group structures in graphs. In Eurographics Conference on Visualization (EuroVis)- STARs, pages 21-40.
  43. Vehlow, C., Reinhardt, T., and Weiskopf, D. (2013). Visualizing fuzzy overlapping communities in networks. Visualization and Computer Graphics, IEEE Transactions on, 19(12):2486-2495.
  44. von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4):395-416.
  45. White, S. and Smyth, P. (2005). A spectral clustering approach to finding communities in graph. InSDM, volume 5, pages 76-84. SIAM.
  46. Xie, J., Kelley, S., and Szymanski, B. K. (2013). Overlapping community detection in networks: The state-ofthe-art and comparative study. Acm computing surveys (csur), 45(4):43.
  47. Zhang, S., Wang, R.-S., and Zhang, X.-S. (2007). Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A: Statistical Mechanics and its Applications, 374(1):483-490.
Download


Paper Citation


in Harvard Style

Sreevalsan-Nair J. and Agarwal S. (2017). NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 140-151. DOI: 10.5220/0006175701400151


in Bibtex Style

@conference{ivapp17,
author={Jaya Sreevalsan-Nair and Shivam Agarwal},
title={NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={140-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006175701400151},
isbn={978-989-758-228-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks
SN - 978-989-758-228-8
AU - Sreevalsan-Nair J.
AU - Agarwal S.
PY - 2017
SP - 140
EP - 151
DO - 10.5220/0006175701400151