Heterogeneous Peer Effects in the Linear Threshold Model


  • Christopher Tran University of Illinois at Chicago
  • Elena Zheleva University of Illinois at Chicago




Data Mining & Knowledge Management (DMKM), Reasoning Under Uncertainty (RU), Machine Learning (ML)


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.




How to Cite

Tran, C., & Zheleva, E. (2022). Heterogeneous Peer Effects in the Linear Threshold Model. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4175-4183. https://doi.org/10.1609/aaai.v36i4.20336



AAAI Technical Track on Data Mining and Knowledge Management