1. 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
 Full text (1,36 MB)
This document has many files! More... |
2. 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
Full text (876,16 KB)
This document has many files! More... |
