Multilayer representation of collaboration networks with higher-order interactions

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.

Application of graph theory in the library domain—Building a faceted framework based on a literature review

“…Recently, the introduction of more elaborate concepts such as multilayer networks (Boccaletti et al, 2014;Kivela et al, 2014;Zingg et al, 2020), hypergraphs (Ouvrard et al, 2017(Ouvrard et al, , 2018, and combinations thereof (Vasilyeva et al, 2021) supplements the view on complex networks (Boccaletti et al, 2006) and calls for new analysis methods (Fezzeh et al, 2021;Pisanski et al, 2020).…”

Dynamics on higher-order networks: a review

“…On the other hand, higher-order network set-ups are used to generalize the formalism of structural controllability to time-varying networks [ 39 ], for which both synthetic and real-world datasets are examined to illustrate the minimum time required to control the concerned systems. Group research collaborations of three or more individuals are illustrated through a higher-order interaction framework and further encoded under multilayer formalism with collaboration data taken from different research disciplines [ 40 ]. Further, heterogeneous dynamics of higher-order structures in time-varying social networks is examined for a number of social datasets [ 41 ].…”

A system model of three-body interactions in complex networks: consensus and conservation

“…In the conventional network representation of complex systems, a dominating hypothesis is that basic interacting units in networks are captured exclusively by pairwise interactions. This assumption is often not verified in diverse real-world systems such as neuroscience [3–5], ecological systems [6,7] and scientific collaboration [8,9], where essential interplay takes place at a collective level involving group or multibody interactions [10–12]. Examples include the presence of higher-order neuronal connection patterns in human brain networks to support nervous activities and cognitive processes [13]; social changes require complex contagions with multiple exposures and group social interactions [14]; and species coexistence and biodiversity can be promoted in an ecosystem if three or more species are factored in the species competition relationship [15].…”