The Structure of Information Pathways in a Social Communication Network

TL;DR

This study analyzes the temporal dynamics of communication in a social network using online email data, revealing how information flows and identifying a sparse backbone that combines strong and long-range ties.

Contribution

It introduces a temporal measure of distance in social networks and defines a backbone structure that highlights key information pathways, offering new structural insights.

Findings

The temporal distance measure reveals structural features not seen in static topology.

The backbone is sparse, with a mix of embedded edges and long-range bridges.

Long-range bridges play a crucial role in information flow.

Abstract

Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate a temporal notion of "distance" in the underlying social network by measuring the minimum time required for information to spread from one node to another -- a concept that draws on the notion of vector-clocks from the study of distributed computing systems. We find that such temporal measures provide structural insights that are not apparent from analyses of the pure social network topology. In particular, we define the network backbone to be the subgraph consisting of edges on which information has the potential to flow the quickest. We find that the backbone is a sparse graph with a concentration of both highly embedded edges and long-range bridges -- a finding that sheds new light on the relationship between tie strength and connectivity in social networks.