У оквиру семинара Центра за изучавање комплексних система Института за физику у Београду, у четвртак, 21. јануара 2021. године у 14 часова путем Zoom платформе, Ана Вранић (Лабораторија за примену рачунара у науци, Институт за физику у Београду) одржаће предавање:
Growth signals determine the topology of evolving networks
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 , 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 , 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 .
In this talk, we will explore how time-varying growth influences the structure of evolving complex networks. We will consider the aging nodes  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 .
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Meeting ID: 826 8858 6903