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Networks are well-established representations of social systems, and temporalnetworks are widely used to study their dynamics. Temporal network data oftenconsist in a succession of static networks over consecutive time windows whoselength, however, is arbitrary, not necessarily corresponding to any intrinsictimescale of the system. Moreover, the resulting view of social networkevolution is unsatisfactory: short time windows contain little information,whereas aggregating over large time windows blurs the dynamics. Going from atemporal network to a meaningful evolving representation of a social networktherefore remains a challenge. Here we introduce a framework to that purpose:transforming temporal network data into an evolving weighted network where theweights of the links between individuals are updated at every interaction. Mostimportantly, this transformation takes into account the interdependence ofsocial relationships due to the finite attention capacities of individuals:each interaction between two individuals not only reinforces their mutualrelationship but also weakens their relationships with others. We study aconcrete example of such a transformation and apply it to several data sets ofsocial interactions. Using temporal contact data collected in schools, we showhow our framework highlights specificities in their structure and temporalorganization. We then introduce a synthetic perturbation into a data set ofinteractions in a group of baboons to show that it is possible to detect aperturbation in a social group on a wide range of timescales and parameters.Our framework brings new perspectives to the analysis of temporal socialnetworks.

From temporal network data to the dynamics of social relationships