Heterogeneous Peer Effects in the Linear Threshold Model

TL;DR

This paper introduces causal inference methods to estimate individual thresholds in the Linear Threshold Model, accounting for heterogeneity in peer effects, leading to improved predictions of information diffusion in social networks.

Contribution

It develops a Structural Causal Model for heterogeneous peer effects and proposes two algorithms for individual threshold estimation using causal trees and meta-learners.

Findings

Models better predict individual thresholds in synthetic data.

Enhanced accuracy in predicting node activation over time.

Applicable to real-world social network datasets.

Abstract

The Linear Threshold Model is a widely used model that describes how information diffuses through a social network. According to this model, an individual adopts an idea or product after the proportion of their neighbors who have adopted it reaches a certain threshold. Typical applications of the Linear Threshold Model assume that thresholds are either the same for all network nodes or randomly distributed, even though some people may be more susceptible to peer pressure than others. To address individual-level differences, we propose causal inference methods for estimating individual thresholds that can more accurately predict whether and when individuals will be affected by their peers. We introduce the concept of heterogeneous peer effects and develop a Structural Causal Model which corresponds to the Linear Threshold Model and supports heterogeneous peer effect identification and estimation. We develop two algorithms for individual threshold estimation, one based on causal trees and one based on causal meta-learners. Our experimental results on synthetic and real-world datasets show that our proposed models can better predict individual-level thresholds in the Linear Threshold Model and thus more precisely predict which nodes will get activated over time.