Moment-Based Estimation of Diffusion and Adoption Parameters in Networks

TL;DR

This paper introduces two novel estimators for network diffusion models that address the computational challenges of maximum likelihood estimation in partially observed networks, enabling more feasible analysis of diffusion processes.

Contribution

It proposes two new estimators tailored for network diffusion models with partial observations, overcoming computational limitations of traditional MLE methods.

Findings

Estimators perform well with large networks.

Reduced computational complexity compared to MLE.

Effective in capturing diffusion parameters with partial data.

Abstract

According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estimation choice, however, it is not always a feasible one. In network diffusion models with unobserved signal propagation, MLE requires integrating out a large number of latent variables, which quickly becomes computationally infeasible even for moderate network sizes and time horizons. Limiting the model time horizon on the other hand entails loss of important information while approximation techniques entail a (small) error that. Searching for a viable alternative is thus potentially highly beneficial. This paper proposes two estimators specifically tailored to the network diffusion model of partially observed adoption and unobserved network diffusion.