The structured backbone of temporal social ties
Teruyoshi Kobayashi, Taro Takaguchi, Alain Barrat

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…
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