The NOESIS Open Source Framework for Network Data Mining

Víctor Martínez, Fernando Berzal, Juan-Carlos Cubero

2015

Abstract

NOESIS is a software framework for the development of data mining techniques for networked data. As an open source project, released under a BSD license, NOESIS intends to provide the necessary infrastructure for solving complex network data mining problems. Currently, it includes a large collection of popular network-related data mining techniques, including the analysis of network structural properties, community detection algorithms, link scoring and prediction methods, and network visualization techniques. The design of NOESIS tries to facilitate the development of parallel algorithms using solid object-oriented design principles and structured parallel programming. NOESIS can be used as a stand-alone application, as many other network analysis packages, and can be included, as a lightweight library, in domain-specific data mining applications and systems.

References

  1. Albert, R. and Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1):47.
  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. Batagelj, V. and Mrvar, A. (1998). Pajek-program for large network analysis. Connections, 21(2):47-57.
  4. Berzal, F., Blanco, I., Cubero, J.-C., and Marin, N. (2002). Component-based data mining frameworks. Communications of the ACM, 45(12):97-100.
  5. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3):75-174.
  6. Herman, I., Melanc¸on, G., and Marshall, M. S. (2000). Graph visualization and navigation in information visualization: A survey. Visualization and Computer Graphics, IEEE Transactions on, 6(1):24-43.
  7. Jackson, M. O. et al. (2008). Social and economic networks. Princeton University Press Princeton.
  8. Lancichinetti, A. and Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E, 80(5):056117.
  9. Liben-Nowell, D. and Kleinberg, J. (2007). The linkprediction problem for social networks. Journal of the American society for information science and technology, 58(7):1019-1031.
  10. L ü, L. and Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6):1150-1170.
  11. Martin, R. C. (2003). Agile Software Development: Principles, Patterns, and Practices. Prentice Hall PTR.
  12. Newman, M. (2010). Networks: An Introduction. Oxford University Press.
  13. Palla, G., Derényi, I., Farkas, I., and Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814-818.
  14. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11):2498-2504.
  15. Smith, M. A., Shneiderman, B., Milic-Frayling, N., Mendes Rodrigues, E., Barash, V., Dunne, C., Capone, T., Perer, A., and Gleave, E. (2009). Analyzing (social media) networks with nodexl. In Proceedings of the fourth international conference on Communities and technologies, pages 255-264. ACM.
  16. Tamassia, R. (2013). Handbook of graph drawing and visualization. CRC press.
  17. Wasserman, S. and Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.
  18. Yang, J. and Leskovec, J. (2013). Overlapping community detection at scale: A nonnegative matrix factorization approach. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM 7813, pages 587-596. ACM.
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Paper Citation


in Harvard Style

Martínez V., Berzal F. and Cubero J. (2015). The NOESIS Open Source Framework for Network Data Mining . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 316-321. DOI: 10.5220/0005610103160321


in Bibtex Style

@conference{kdir15,
author={Víctor Martínez and Fernando Berzal and Juan-Carlos Cubero},
title={The NOESIS Open Source Framework for Network Data Mining},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={316-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005610103160321},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - The NOESIS Open Source Framework for Network Data Mining
SN - 978-989-758-158-8
AU - Martínez V.
AU - Berzal F.
AU - Cubero J.
PY - 2015
SP - 316
EP - 321
DO - 10.5220/0005610103160321