DySuse: Susceptibility Estimation in Dynamic Social Networks
Yingdan Shi Jingya Zhou Congcong Zhang
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.