Tempus Fugit: The Impact of Time in Knowledge Mobilization Networks

TL;DR

This paper introduces a temporal betweenness measure to analyze knowledge mobilization networks, revealing hidden dynamic roles of nodes that static analysis overlooks, thereby enhancing understanding of time's impact in social networks.

Contribution

It proposes a novel temporal betweenness measure and demonstrates its effectiveness in uncovering dynamic centrality roles in social networks that static measures miss.

Findings

Temporal analysis reveals hidden centrality roles of nodes.

Nodes with low static centrality can have high temporal importance.

The approach enhances understanding of time's role in social network dynamics.

Abstract

The temporal component of social networks is often neglected in their analysis, and statistical measures are typically performed on a "static" representation of the network. As a result, measures of importance (like betweenness centrality) cannot reveal any temporal role of the entities involved. Our goal is to start filling this limitation by proposing a form of temporal betweenness measure, and by using it to analyse a knowledge mobilization network. We show that this measure, which takes time explicitly into account, allows us to detect centrality roles that were completely hidden in the classical statistical analysis. In particular, we uncover nodes whose static centrality was considered negligible, but whose temporal role is instead important to accelerate mobilization flow in the network. We also observe the reverse behaviour by detecting nodes with high static centrality, whose role as temporal bridges is instead very low. By revealing important temporal roles, this study is a first step towards a better understanding of the impact of time in social networks, and opens the road to further investigation.