论文标题
线性阈值模型中的异构同伴效应
Heterogeneous Peer Effects in the Linear Threshold Model
论文作者
论文摘要
线性阈值模型是一个广泛使用的模型，它描述了信息如何通过社交网络扩散。根据该模型，一个人在采用邻居的比例之后采用了一个想法或产品，达到了一定的门槛。线性阈值模型的典型应用假设所有网络节点的阈值都是相同的，或者是随机分布的，即使某些人可能比其他人更容易受到同伴压力的影响。为了解决个人级别的差异，我们提出了因果推理方法，以估计可以更准确地预测个人是否以及何时受同龄人影响的个体阈值。我们介绍了异构同伴效应的概念，并开发了与线性阈值模型相对应的结构因果模型，并支持异质的同伴效应识别和估计。我们开发了两种用于个体阈值估计的算法，一种基于因果树，一种基于因果元学习者。我们对合成和现实世界数据集的实验结果表明，我们提出的模型可以更好地预测线性阈值模型中的个体级阈值，因此更精确地预测了哪些节点会随着时间的推移而被激活。
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.
