DySuse: Susceptibility Estimation in Dynamic Social Networks

• URL: http://arxiv.org/abs/2308.10442v1
• Date: Mon, 21 Aug 2023 03:28:34 GMT
• Title: DySuse: Susceptibility Estimation in Dynamic Social Networks
• Authors: Yingdan Shi, Jingya Zhou, Congcong Zhang
• Abstract summary: We propose a task, called susceptibility estimation in dynamic social networks, which is more realistic and valuable in real-world applications. We leverage a structural feature module to independently capture the structural information of influence diffusion on each single graph snapshot. Our framework is superior to the existing dynamic graph embedding models and has satisfactory prediction performance in multiple influence diffusion models.
• Score: 2.736093604280113
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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