A COMPUTER SOFTWARE FOR THE EDUCATION OF COMPLEX NETWORK ANALYSIS

Complex network analysis is an attractive tool for capturing the self-organizing principles underlying the social, physical or biological communities. Several software are developed for either analyzing or generating complex networks, including the visualization utilities. We developed an open source software in Microsoft .NET platform for generating networks based on the most common models as Barabasi-Albert, Erdos-Renyi, Watts-Strogatz including the analyzing utilities defining the network like average separation, degree distribution, clustering coefficient etc. In contrast with the well-known software, this software aims to contribute the understanding of the underlying mechanisms of complex networks. It also forms a basis to further developments that should provide an extensive view to network construction. As an open source software, the opportunity of editing the core functions about network dynamics offer a pedagogical approach to learn more about self-organizing networks.

[1]  A. Barabasi,et al.  Evolution of the social network of scientific collaborations , 2001, cond-mat/0104162.

[2]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Woo-Sung Jung,et al.  Quantitative and empirical demonstration of the Matthew effect in a study of career longevity , 2008, Proceedings of the National Academy of Sciences.

[5]  İlker Türker,et al.  Scientific collaboration network of Turkey , 2013 .

[6]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[7]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  İlker Türker,et al.  Patterns of collaboration in four scientific disciplines of the Turkish collaboration network , 2014 .

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[13]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Matjaz Perc,et al.  Growth and structure of Slovenia's scientific collaboration network , 2010, J. Informetrics.