The structured backbone of temporal social ties

TL;DR

This paper introduces a novel method for extracting the significant backbone of temporal social networks by using a null model that accounts for node activity, enabling identification of meaningful interactions over time.

Contribution

The authors develop a temporal null model to identify statistically significant ties and triads in time-resolved networks, surpassing static aggregation methods.

Findings

More significant ties identified than in static methods

Most significant ties are intra-community edges

Significant triads differ from static triangles

Abstract

In many data sets, crucial information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems, for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections, the "network backbone", is a non-trivial task, and methods put forward until now do not address time-resolved networks, whose availability has strongly increased in recent years. We develop here such a method, by defining an adequate temporal network null model, which calculates the random chance of nodes to be connected at any time after controlling for their activity. This allows us to identify, at any level of statistical significance, pairs of nodes that have more interactions than expected given their activities: These form a backbone of significant ties. We apply our method to empirical temporal networks of socio-economic interest and find that (i) at given level of statistical significance, our method identifies more significant ties than methods considering temporally aggregated networks, and (ii) when a community structure is present, most significant ties are intra-community edges, suggesting that the weights of inter-community edges can be explained by the null model of random interactions. Most importantly, our filtering method can assign a significance to more complex structures such as triads of simultaneous interactions, while methods based on static representations are by construction unable to do so. Strikingly, we uncover that significant triads are not equivalent to triangles composed by three significant edges. Our results hint at new ways to represent temporal networks for use in data-driven models and in anonymity-preserving ways.