Inferring Network Structure from Cascades

TL;DR

This paper introduces three topological methods to infer directed network structures from observed cascade data, applicable to various models with high success on synthetic and real networks.

Contribution

It presents novel formulas for network inference from cascades that work under broad activation probability models, advancing the analysis of network dynamics.

Findings

High success rates on synthetic networks

Effective inference on real-world networks

Applicable to diverse cascade models

Abstract

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.