Inferring networks of diffusion and influence

Abstract

Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, 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 in practice gives provably near-optimal performance.We demonstrate the effectiveness of our approach by tracing information cascades 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 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.

MeiKe: Influence-based Communities in Networks

“…We expect to find media nodes as: media websites/accounts in MemeTracker and Twitter, survey papers in Citation, and people who cover multiple areas/departments in Coauthor, Google+ and Enron. We learn the weights of MemeTracker from blog cascades [7] and normalize the number of emails for Enron as edge weights. For others, we set them to be the same as w ij = 0.02 following literature [21].…”

“…Similarly, for MemeTracker, we pick high web traffic websites which cover more than two topics (like sports and entertainment) as media nodes. For kernel communities, we pick websites in each area that have spread the most memes from the original cascades [7] as the ground truth. Parameters.…”

Crowd-Sensing with Polarized Sources

“…Netrapalli and Sanghavi [9], Myers and Leskovec [15], and Rodriguez et al [16] model the propagation of information through social networks as epidemic cascades and use different ways to estimate the propagation graph from multiple cascades. This work nicely complements ours, since the latent influence propagation network is one of the inputs to our maximum likelihood (credibility) estimator.…”

Society's Nervous System: Building Effective Government, Energy, and Public Health Systems

“…It is described in more detail in the Appendix. Other related models exist, and some have similar properties [11,12,15,16,19,20,21,22,23,24,25,26,27]. The core concept here is that the structure of the model matches the structure of the social phenomena and that the model and its dynamics are driven by real-world observational data.…”