DySuse: Susceptibility Estimation in Dynamic Social Networks

TL;DR

This paper introduces DySuse, a novel framework for estimating individual susceptibility to influence in dynamic social networks, addressing a previously unexplored and computationally challenging problem with promising results.

Contribution

The paper proposes the first susceptibility estimation method in dynamic social networks using a new end-to-end framework with structural, temporal, and attention mechanisms.

Findings

Outperforms existing dynamic graph embedding models

Accurately predicts influence susceptibility in various diffusion models

Demonstrates practical value in real-world social network analysis

Abstract

Influence estimation aims to predict the total influence spread in social networks and has received surged attention in recent years. Most current studies focus on estimating the total number of influenced users in a social network, and neglect susceptibility estimation that aims to predict the probability of each user being influenced from the individual perspective. As a more fine-grained estimation task, susceptibility estimation is full of attractiveness and practical value. Based on the significance of susceptibility estimation and dynamic properties of social networks, we propose a task, called susceptibility estimation in dynamic social networks, which is even more realistic and valuable in real-world applications. Susceptibility estimation in dynamic networks has yet to be explored so far and is computationally intractable to naively adopt Monte Carlo simulation to obtain the results. To this end, we propose a novel end-to-end framework DySuse based on dynamic graph embedding technology. Specifically, we leverage a structural feature module to independently capture the structural information of influence diffusion on each single graph snapshot. Besides, {we propose the progressive mechanism according to the property of influence diffusion,} to couple the structural and temporal information during diffusion tightly. Moreover, a self-attention block {is designed to} further capture temporal dependency by flexibly weighting historical timestamps. Experimental results show that our framework is superior to the existing dynamic graph embedding models and has satisfactory prediction performance in multiple influence diffusion models.