%A ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin %T Review of Information Cascade Prediction Methods Based on Deep Learning %0 Journal Article %D 2020 %J Computer Science %R 10.11896/jsjkx.200300130 %P 141-153 %V 47 %N 7 %U {https://www.jsjkx.com/CN/abstract/article_19254.shtml} %8 2020-07-15 %X Online social media greatly promotes the generation and transmission of information,exacerbates the communication and interaction between massive amounts of information,and highlights the importance of predicting information cascades.In recent years,deep learning has been widely used in the field of information cascade prediction.This paper mainly classifies,sorts,and summarizes the current research status of deep learning-based information cascade prediction methods and classic algorithms.According to the different emphasis of information cascade feature characterization,the information cascade prediction method based on deep learning is divided into time series information cascade prediction method and topology information cascade prediction method.The time series information cascade prediction method is further divided into methods based on random walks and methods based on diffusion paths,and the topology information cascade prediction method is divided into methods based on global topological structure and methods based on neighborhood aggregation.This paper details the principles and advantages and disadvantages of each type of method,and introduces the data sets and evaluation indicators commonly used in the field of information cascade prediction,and compares the information cascade prediction algorithms based on deep learning in the macro and micro information cascade prediction scenarios,and discusses some technical details commonly used in information cascade prediction algorithms.Finally,this paper summarizes the field possible future research directions and development trends.