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.

翻译：影响力估计旨在预测社交网络中的总影响力传播，近年来受到广泛关注。当前研究多聚焦于计算社交网络中被影响用户的总数，而忽略了旨在从个体角度预测每个用户被影响概率的易感性估计。作为更细粒度的估计任务，易感性估计兼具吸引力和实用价值。基于易感性估计的重要性及社交网络的动态特性，我们提出一项称为"动态社交网络中的易感性估计"任务，该任务在真实应用中更具现实意义与价值。目前动态网络中的易感性估计尚未得到探索，且直接采用蒙特卡洛模拟获取结果在计算上不可行。为此，我们提出基于动态图嵌入技术的端到端框架DySuse。具体而言，利用结构特征模块独立获取每个图快照上影响力扩散的结构信息，同时根据影响力扩散特性提出渐进机制以紧密耦合扩散过程中的结构与时间信息。此外，设计自注意力模块通过灵活加权历史时间戳进一步捕获时间依赖性。实验结果表明，该框架优于现有动态图嵌入模型，并在多种影响力扩散模型中展现出令人满意的预测性能。