Time varying networks and the weakness of strong ties

TL;DR

This paper analyzes time-varying social networks, introduces a model capturing strong and weak ties, and finds that strong ties can hinder information spread in dynamic networks.

Contribution

It presents a new statistical law for temporal evolution of egocentric networks and a reinforcement model for time-varying networks with implications for information diffusion.

Findings

Strong ties inhibit rumor spreading in dynamic networks.

A simple statistical law characterizes the evolution of egocentric networks.

Time-varying interactions significantly affect information diffusion.

Abstract

In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.