Inferring Networks of Diffusion and Influence

TL;DR

This paper introduces a scalable method to infer diffusion networks from infection times, enabling the analysis of information flow and influence patterns in large-scale online media datasets.

Contribution

It develops an efficient approximation algorithm for inferring networks of diffusion and influence from observed infection times, addressing NP-hard optimization challenges.

Findings

Identified core-periphery structure in online news diffusion networks.

Revealed stable influence circles among major media sites.

Analyzed information flow across 170 million online articles.

Abstract

Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.