Modeling Information Propagation with Survival Theory

TL;DR

This paper introduces survival theory-based models for inferring networks from contagion spread data, enabling efficient and flexible modeling of complex contagion dynamics.

Contribution

It presents a unified framework with additive and multiplicative risk models that generalize existing methods and handle both positive and negative node influence.

Findings

Models accurately predict cascade length and duration.

Efficient inference due to convex optimization.

Multiplicative model captures both risk increase and decrease.

Abstract

Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.