SCL Online Seminar by Ana Vranić

You are cordially invited to the SCL online seminar of the Center for the Study of Complex Systems, which will be held on Thursday, 21 January 2021 at 14:00 on Zoom. The talk entitled

Growth signals determine the topology of evolving networks

will be given by Ana Vranić (Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade). Abstract of the talk:

Network science provides us a theoretical framework for representing and studying various complex systems, including biological, technological, and social ones. These systems are composed of many units that interact with each other, and their collective behavior cannot be predicted from the behavior of individual elements. The structure of complex networks is essential for understanding the evolution and function of complex systems. Regardless of the different origins of complex networks in nature and society, it was shown that they share similar properties [1], such as broad degree distribution, degree-degree correlations, and they are clustered. Growing network models are often used for exploring the dynamics and topology of complex networks. Network growth, in combination with linking rules, shapes the topology of a network. For example, in the Barabási-Albert model [2], growth and preferential attachment lead to broad degree distribution networks. So far, the focus was mostly on various linking rules and their influence on network structure. The majority of the models assume that the network growth is constant, i.e., at each time step, one new node is introduced. However, the growth of real systems is anything but constant. It varies in time, has trends and cycles, and long-range temporal correlations [3].

In this talk, we will explore how time-varying growth influences the structure of evolving complex networks. We will consider the aging nodes [4] model and include time-varying network growth. We will use different real and computer-generated time-varying growth signals to generate complex networks. Afterwards, we will compare the structure of these networks with the ones obtained with constant growth signals, and show that the properties of the growth signal significantly determine the topology of the obtained networks. Our results indicate that time-varying growth should be considered as a parameter in models of complex systems [5].

[1] Boccaletti S., Latora V., Moreno Y., Chavez M., and Hwang D.U., Phys. Rep. 424, 175 (2006).
[2] Barabási A. L. and Albert R., Science 286, 509 (1999).
[3] Mitrović Dankulov M., Melnik R., and Tadić B., Sci. Rep. 5, 12197 (2015).
[4] Hajra K. B. and Sen P., Phys. Rev. E 70, 056103 (2004).
[5] Vranić A. and Mitrović Dankulov M., accepted in JSTAT, arXiv:2009.00444 (2020).

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