Experiments in Inferring Social Networks of Diffusion

TL;DR

This paper evaluates the NETINF algorithm's ability to infer social network structures from observed information diffusion times across various online platforms, highlighting its applicability and limitations.

Contribution

It investigates the performance of the NETINF algorithm across diverse social network topologies, extending its evaluation beyond previous settings.

Findings

NETINF can effectively infer network structures from diffusion data.

Performance varies depending on network topology and platform.

The study demonstrates the algorithm's versatility across different social media environments.

Abstract

Information diffusion is a fundamental process that takes place over networks. While it is rarely realistic to observe the individual transmissions of the information diffusion process, it is typically possible to observe when individuals first publish the information. We look specifically at previously published algorithm NETINF that probabilistically identifies the optimal network that best explains the observed infection times. We explore how the algorithm could perform on a range of intrinsically different social and information network topologies, from news blogs and websites to Twitter to Reddit.