From temporal network data to the dynamics of social relationships

Abstract

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group’s evolution remains a challenge. Indeed, the temporal network at any specific time contains only the interactions taking place at that time and aggregating on successive time-windows also has important limitations. Here, we present a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent: as the time we can invest in social relationships is limited, reinforcing a relationship with someone is done at the expense of our relationships with others. We implement this interdependence in a parsimonious two-parameter model and apply it to several human and non-human primates’ datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks. Our model solves a long-standing problem by providing a simple and natural way to describe the dynamic evolution of social networks, with far-reaching consequences for the study of social networks and social evolution.

Neighbourhood matching creates realistic surrogate temporal networks

“…These features are instead well captured by more theoretical models of network generation that include aging, 52,53 edge reinforcement, 54,55 or in general some mechanism for memory such that contact durations and inter-event times are heterogeneous and depend on the past interactions. 56,57 Memory can also be used to generate a synthetic temporal network that is organized in groups, i.e.…”

Modeling framework unifying contact and social networks

“…The main social mechanisms implemented in such models include (i) reinforcement processes, where the probability for two nodes to interact with each other increases after each interaction, leading to broad distributions of contact durations and edge weights in the aggregated network [17,19,20]; (ii) triadic closure, which states that a node is more likely to interact with a neighbour of a neighbour, and has been shown to account for the high clustering coefficient of the aggregated network; (iii) "memory loss process", which can be random or target unused social ties [21,22], and contributes to the emergence of community structure in the aggregated network of social systems [21,23,24].…”

“…First, B(t) guides the interactions that will take place at t, i.e., influences the edges of G(t). Second, interactions have an impact on social bonds through a reinforcement mechanism [22]: if an interaction occurs between i and j, then B ij increases. Moreover, we take into account that the time and energy spent to maintain the tie with an individual is taken from a finite interaction capacity and is thus time not spent with others [28,29].…”

“…Moreover, we take into account that the time and energy spent to maintain the tie with an individual is taken from a finite interaction capacity and is thus time not spent with others [28,29]. Therefore, if i and j do not interact but i interacts with another agent k at t, B ij decreases [22].…”

Dynamics of the US domestic airline network during the COVID-19 pandemic

“…Recurrence refers to the return of the network structure to something close to the past structure (Masuda and Holme, 2019). The recurrence in the evolution of the network structure has been investigated for some real-world network systems (Masuda and Holme, 2019;Cruickshank and Carley, 2020;Gelardi et al, 2021;Sugishita and Masuda, 2021). However, to the best of our knowledge, there is no example of its application to the analysis of the airline networks during the COVID-19 pandemic.…”