TL;DR
This paper introduces a novel framework for transforming temporal social network data into an evolving weighted network that accounts for social relationship interdependence and individual attention limits, improving analysis of social dynamics.
Contribution
The authors propose a new method to convert temporal networks into evolving weighted networks considering social interdependence and attention constraints, enhancing the understanding of social dynamics.
Findings
Framework highlights structural and temporal features of social networks.
Method detects social perturbations across multiple timescales.
Application to real and synthetic data demonstrates effectiveness.
Abstract
Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
