Browsing by Author "Vranić, Ana"

Now showing 1 - 5 of 5
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    Charge transport in the Hubbard model at high temperatures: Triangular versus square lattice
    Vranić, Ana; Vučičević, Jakša; Kokalj, Jure; Skolimowski, Jan; Žitko, Rok; Mravlje, Jernej; Tanasković, Darko
    High-temperature bad-metal transport has been recently studied both theoretically and in experiments as one of the key signatures of strong electronic correlations. Here we use the dynamical mean field theory and its cluster extensions, as well as the finite-temperature Lanczos method to explore the influence of lattice frustration on the thermodynamic and transport properties of the Hubbard model at high temperatures. We consider the triangular and the square lattices at half-filling and at 15% hole doping. We find that for T greater than or similar to 1.5t the self-energy becomes practically local, while the finite-size effects become small at lattice size 4x4 for both lattice types and doping levels. The vertex corrections to optical conductivity, which are significant on the square lattice even at high temperatures, contribute less on the triangular lattice. We find approximately linear temperature dependence of dc resistivity in doped Mott insulator for both types of lattices.
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    Growth signals determine the topology of evolving networks
    Vranić, Ana; Mitrović Dankulov, Marija
    Network science provides an indispensable theoretical framework for studying the structure and function of real complex systems. Different network models are often used for finding the rules that govern their evolution, whereby the correct choice of model details is crucial for obtaining relevant insights. Here, we study how the structure of networks generated with the aging nodes model depends on the properties of the growth signal. We use different fluctuating signals and compare structural dissimilarities of the networks with those obtained with a constant growth signal. We show that networks with power-law degree distributions, which are obtained with time-varying growth signals, are correlated and clustered, while networks obtained with a constant growth signal are not. Indeed, the properties of the growth signal significantly determine the topology of the obtained networks and thus ought to be considered prominently in models of complex systems.
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    Sustainability of Stack Exchange Q&A communities: the role of trust
    Vranić, Ana; Tomašević, Aleksandar; Alorić, Aleksandra; Mitrović Dankulov, Marija
    Knowledge-sharing communities are fundamental elements of a knowledge-based society. Understanding how different factors influence their sustainability is of crucial importance. We explore the role of the social network structure and social trust in their sustainability. We analyze the early evolution of social networks in four pairs of active and closed Stack Exchange communities on topics of physics, astronomy, economics, and literature and use a dynamical reputation model to quantify the evolution of social trust in them. In addition, we study the evolution of two active communities on mathematics topics and two closed communities about startups and compare them with our main results. Active communities have higher local cohesiveness and develop stable, better-connected, trustworthy cores. The early emergence of a stable and trustworthy core may be crucial for sustainable knowledge-sharing communities.
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    Universal growth of social groups: empirical analysis and modeling
    Vranić, Ana; Smiljanić, Jelena; Mitrović Dankulov, Marija
    Social groups are fundamental elements of any social system. Their emergence and evolution are closely related to the structure and dynamics of a social system. Research on social groups was primarily focused on the growth and the structure of the interaction networks of social system members and how members’ group affiliation influences the evolution of these networks. The distribution of groups’ size and how members join groups has not been investigated in detail. Here we combine statistical physics and complex network theory tools to analyze the distribution of group sizes in three data sets, Meetup groups based in London and New York and Reddit. We show that all three distributions exhibit log-normal behavior that indicates universal growth patterns in these systems. We propose a theoretical model that combines social and random diffusion of members between groups to simulate the roles of social interactions and members’ interest in the growth of social groups. The simulation results show that our model reproduces growth patterns observed in empirical data. Moreover, our analysis shows that social interactions are more critical for the diffusion of members in online groups, such as Reddit, than in offline groups, such as Meetup. This work shows that social groups follow universal growth mechanisms that need to be considered in modeling the evolution of social systems.
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    Universal growth of social groups: empirical analysis and modeling
    Vranić, Ana; Smiljanić, Jelena; Mitrović Dankulov, Marija
    Social groups are fundamental elements of any social system. Their emergence and evolution are closely related to the structure and dynamics of a social system. Research on social groups was primarily focused on the growth and the structure of the interaction networks of social system members and how members’ group affiliation influences the evolution of these networks. The distribution of groups’ size and how members join groups has not been investigated in detail. Here we combine statistical physics and complex network theory tools to analyze the distribution of group sizes in three data sets, Meetup groups based in London and New York and Reddit. We show that all three distributions exhibit log-normal behavior that indicates universal growth patterns in these systems. We propose a theoretical model that combines social and random diffusion of members between groups to simulate the roles of social interactions and members’ interest in the growth of social groups. The simulation results show that our model reproduces growth patterns observed in empirical data. Moreover, our analysis shows that social interactions are more critical for the diffusion of members in online groups, such as Reddit, than in offline groups, such as Meetup. This work shows that social groups follow universal growth mechanisms that need to be considered in modeling the evolution of social systems.