1. Collective dynamics of swarmalators with higher-order interactionsMd Sayeed Anwar, Gourab Kumar Sar, Matjaž Perc, Dibakar Ghosh, 2024, original scientific article Abstract: Higher-order interactions shape collective dynamics, but how they affect transitions between different states in swarmalator systems is yet to be determined. To that effect, we here study an analytically tractable swarmalator model that incorporates both pairwise and higher-order interactions, resulting in four distinct collective states: async, phase wave, mixed, and sync states. We show that even a minute fraction of higher-order interactions induces abrupt transitions from the async state to the phase wave and the sync state. We also show that higher-order interactions facilitate an abrupt transition from the phase wave to the sync state bypassing the intermediate mixed state. Moreover, elevated levels of higher-order interactions can sustain the presence of phase wave and sync state, even when pairwise interactions lean towards repulsion. The insights gained from these findings unveil self-organizing processes that hold the potential to explain sudden transitions between various collective states in numerous real-world systems.
Keywords: collective dynamics, nonlinear oscillator, higher-order interactions, complex network, statistical physics
Published in DKUM: 07.05.2025; Views: 0; Downloads: 1
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2. The microdynamics shaping the relationship between democracy and corruptionBoris Podobnik, Marko Jusup, Dean Korošak, Petter Holme, Tomislav Lipić, 2022, original scientific article Abstract: Physics has a long tradition of laying rigorous quantitative foundations for social phenomena. Here, we up the ante for physics' forays into the territory of social sciences by (i) empirically documenting a tipping point in the relationship between democratic norms and corruption suppression, and then (ii) demonstrating how such a tipping point emerges from a micro-scale mechanistic model of spin dynamics in a complex network. Specifically, the tipping point in the relationship between democratic norms and corruption suppression is such that democratization has little effect on suppressing corruption below a critical threshold, but a large effect above the threshold. The micro-scale model of spin dynamics underpins this phenomenon by reinterpreting spins in terms of unbiased (i.e. altruistic) and biased (i.e. parochial) other-regarding behaviour, as well as the corresponding voting preferences. Under weak democratic norms, dense social connections of parochialists enable coercing enough opportunist voters to vote in favour of perpetuating parochial in-group bias. Society may, however, strengthen democratic norms in a rapid turn of events during which opportunists adopt altruism and vote to subdue bias. The emerging model outcome at the societal scale thus mirrors the data, implying that democracy either perpetuates or suppresses corruption depending on the prevailing democratic norms.
Keywords: tipping point, complex network, sociophysics
Published in DKUM: 13.03.2025; Views: 0; Downloads: 2
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3. Dissimilarity-driven behavior and cooperation in the spatial public goods gameYinhai Fang, Tina Perc Benko, Matjaž Perc, Haiyan Xu, 2019, original scientific article Abstract: In this paper, we explore the impact of four different types of dissimilarity-driven behavior on the evolution of cooperation in the spatial public goods game. While it is commonly assumed that individuals adapt their strategy by imitating one of their more successful neighbors, in reality only very few will be awarded the highest payoffs. Many have equity or equality preferences, and they have to make do with an average or even with a low payoff. To account for this, we divide the population into two categories. One consists of payoff-driven players, while the other consists of dissimilarity-driven players. The later imitate the minority strategy in their group based on four different dissimilaritydriven behaviors. The rule that most effectively promotes cooperation, and this regardless of the multiplication factor of the public goods game, is when individuals adopt the minority strategy only when their payoff is better than that of their neighbors. If the dissimilarity-driven players adopt the minority strategy regardless of the payoffs of others, or if their payoff is the same, the population typically evolves towards a neutral state where cooperators and defectors are equally common. This may be beneficial when the multiplication factor is low, when defectors would otherwise dominate. However, if the dissimilarity-driven players adopt the minority strategy only when their payoff is worse than that of their neighbors, then cooperation is not promoted at all in comparison to the baseline case in the absence of dissimilarity-driven behavior. We explore the pattern formation behind these results, and we discuss their wider implications for the better understanding of cooperative behavior in social groups.
Keywords: theoretical biology, evolution, agent-based modeling, complex system, network science, evolutionary game theory
Published in DKUM: 26.02.2025; Views: 0; Downloads: 4
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4. Identification of influential invaders in evolutionary populationsGuoli Yang, Tina Perc Benko, Matteo Cavaliere, Jincai Huang, Matjaž Perc, 2019, original scientific article Abstract: The identification of the most influential nodes has been a vibrant subject of research across the whole of network science. Here we map this problem to structured evolutionary populations, where strategies and the interaction network are both subject to change over time based on social inheritance. We study cooperative communities, which cheaters can invade because they avoid the cost of contributions that are associated with cooperation. The question that we seek to answer is at which nodes cheaters invade most successfully. We propose the weighted degree decomposition to identify and rank the most influential invaders. More specifically, we distinguish two kinds of ranking based on the weighted degree decomposition. We show that a ranking strategy based on negative-weighted degree allows to successfully identify the most influential invaders in the case of weak selection, while a ranking strategy based on positive-weighted degree performs better when the selection is strong. Our research thus reveals how to identify the most influential invaders based on statistical measures in dynamically evolving cooperative communities.
Keywords: theoretical biology, evolution, agent-based modeling, complex system, network science, evolutionary game theory
Published in DKUM: 26.02.2025; Views: 0; Downloads: 4
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5. Association between productivity and journal impact across disciplines and career ageAndre S. Sunahara, Matjaž Perc, Haroldo V. Ribeiro, 2021, original scientific article Abstract: The association between productivity and impact of scientific production is a long-standing debate in science that remains controversial and poorly understood. Here we present a large-scale analysis of the association between yearly publication numbers and average journal-impact metrics for the Brazilian scientific elite. We find this association to be discipline specific, career age dependent, and similar among researchers with outlier and nonoutlier performance. Outlier researchers either outperform in productivity or journal prestige, but they rarely do so in both categories. Nonoutliers also follow this trend and display negative correlations between productivity and journal prestige but with discipline-dependent intensity. Our research indicates that academics are averse to simultaneous changes in their productivity and journal-prestige levels over consecutive career years. We also find that career patterns concerning productivity and journal prestige are discipline-specific, having in common a raise of productivity with career age for most disciplines and a higher chance of outperforming in journal impact during early career stages.
Keywords: network, cooperation, social physics, complex system
Published in DKUM: 10.12.2024; Views: 0; Downloads: 8
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6. Multilayer representation of collaboration networks with higher-order interactionsE. Vasilyeva, A. Kozlov, Karin Alfaro-Bittner, D. Musatov, A. M. Raigorodskii, Matjaž Perc, Stefano Boccaletti, 2021, original scientific article Abstract: Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.
Keywords: network, collaboration, social physics, complex system
Published in DKUM: 18.10.2024; Views: 0; Downloads: 1
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7. Synchronization in simplicial complexes of memristive Rulkov neuronsMahtab Mehrabbeik, Sajad Jafari, Matjaž Perc, 2023, original scientific article Abstract: Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure pairwise hybrid (electrical and chemical) synapses alongside a network with two-node electrical and multi-node chemical connections. The findings provide good insight into the impact of incorporating higher-order interaction in a network. Compared to two-node chemical synapses, higher-order interactions adjust the synchronization patterns to lower multi-node chemical coupling parameter values. Furthermore, the effect of altering higher-order coupling parameter value on the dynamics of neurons in the synchronization state is researched. It is also shown how increasing network size can enhance synchronization by lowering the value of coupling parameters whereby synchronization occurs. Except for complete synchronization, cluster synchronization is detected for higher electrical coupling strength values wherein the neurons are out of the completed synchronization state.
Keywords: simplicial complex, higher-order network, memristive Rulkov, synchronization, cluster synchronization
Published in DKUM: 11.09.2024; Views: 37; Downloads: 9
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8. Why are there six degrees of separation in a social network?I. Samoylenko, D. Aleja, E. Primo, Karin Alfaro-Bittner, E. Vasilyeva, K. Kovalenko, D. Musatov, A. M. Raigorodskii, R. Criado, M. Romance, David Papo, Matjaž Perc, B. Barzel, Stefano Boccaletti, 2023, original scientific article Abstract: A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.
Keywords: degree distribution, network evolution, complex network, small-world network, social physics
Published in DKUM: 16.07.2024; Views: 111; Downloads: 18
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9. Segregation dynamics driven by network leadersWen-Xuan Wang, Yuhao Feng, Siru Chen, Wenzhe Xu, Xinjian Zhuo, Huijia Li, Matjaž Perc, 2022, original scientific article Abstract: Network segregation - a critical problem in real-life networks - can reveal the emergence of conflicts or signal an impending collapse of the whole system. However, the strong heterogeneity of such networks and the various definitions for key nodes continue to pose challenges that limit our ability to foresee segregation and to determine the main drivers behind it. In this paper, we show that a multi-agent leader-follower consensus system can be utilized to define a new index, named leadership, to identify key leaders in real-life networks. And then, this paper explores the emergence of network segregation that is driven by these leaders based on the removal or the rewiring of the relations between different nodes in agreement with their contribution distance. We finally show that the observed leaders-driven segregation dynamics reveals the dynamics of heterogeneous attributes that critically influence network structure and its segregation. Thus, this paper provides a theoretical method to study complex social interactions and their roles in network segregation, which ultimately leads to a closed-form explanation for the emergence of imbalanced network structure from an evolutionary perspective.
Keywords: complex networks, network segregation, multi-agent leader–follower consensus system, key leaders identification, leader, segregation, social physics
Published in DKUM: 08.07.2024; Views: 122; Downloads: 27
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10. Reputation and reciprocity : reviewChengyi Xia, Juan Wang, Matjaž Perc, Zhen Wang, 2023, review article Abstract: Reputation and reciprocity are key mechanisms for cooperation in human societies, often going hand in hand to favor prosocial behavior over selfish actions. Here we review recent researches at the interface of physics and evolutionary game theory that explored these two mechanisms. We focus on image scoring as the bearer of reputation, as well as on various types of reciprocity, including direct, indirect, and network reciprocity. We review different definitions of reputation and reciprocity dynamics, and we show how these affect the evolution of cooperation in social dilemmas. We consider first-order, second-order, as well as higherorder models in well-mixed and structured populations, and we review experimental works that support and inform the results of mathematical modeling and simulations. We also provide a synthesis of the reviewed researches along with an outlook in terms of six directions that seem particularly promising to explore in the future.
Keywords: pattern formation, Monte Carlo method, complex network, evolutionary game theory, cooperation, social physics
Published in DKUM: 20.06.2024; Views: 154; Downloads: 49
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