基于深度学习的信息级联预测方法综述

doi:10.11896/jsjkx.200300130

计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 141-153.doi: 10.11896/jsjkx.200300130

基于深度学习的信息级联预测方法综述

张志扬, 张凤荔, 谭琪, 王瑞锦

电子科技大学信息与软件工程学院成都 610054

收稿日期:2020-03-23 出版日期:2020-07-15 发布日期:2020-07-16
通讯作者: 张凤荔(fzhang@uestc.edu.cn)
作者简介:13980044734@163.com
基金资助:
国家自然科学基金(61802033,61472064,61602096);四川省科技计划(2018GZ0087,2019YJ0543);四川省区域创新合作项目(2020YFQ0018);博士后基金项目(2018M643453);广东省国家重点实验室项目(2017B030314131);网络与数据安全四川省重点实验室开放课题(NDSMS201606)

Review of Information Cascade Prediction Methods Based on Deep Learning

ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin

School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China

Received:2020-03-23 Online:2020-07-15 Published:2020-07-16
About author:ZHANG Zhi-yang,born in 1997,postgraduate,is a member of China Computer Federation.His main research interests include machine learning,data mining and cascade prediction.
ZHANG Feng-li,born in 1963,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include network security and network engineering,cloud computing and big data and machine learning.
Supported by:
This work was supported by the National Natural Science Foundation of China(61802033,61472064,61602096),Sichuan Science and Technology Program (2018GZ0087,2019YJ0543),Sichuan Regional Innovation Cooperation Project(2020YFQ0018),Chinese Postdoctoral Science Foundation(2018M643453), Guangdong Provincial Key Laboratory Project(2017B030314131) and Network and Data Security Key Laboratory of Sichuan Province Open Issue(NDSMS201606)

摘要/Abstract

摘要： 在线社交媒体极大地促进了信息的产生和传递,加速了海量信息之间的传播与交互,使预测信息级联的重要性逐渐突显。近年来,深度学习已经被广泛用于信息级联预测(Information Cascade Prediction)领域。文中主要对基于深度学习的信息级联预测方法的研究现状与经典算法进行分类、梳理与总结。根据信息级联特征刻画的侧重点不同,将基于深度学习的信息级联预测方法分为时序信息级联预测方法与拓扑信息级联预测方法,并进一步将时序信息级联预测方法分为基于随机游走(Random Walk)的方法与基于扩散路径的方法,将拓扑信息级联预测方法分为基于全局拓扑结构的方法与基于邻域聚合的方法;并对每类方法进行详细的原理阐述与优缺点介绍,介绍了信息级联预测领域常用的数据集与评价指标,在宏观与微观两种信息级联预测场景下对基于深度学习的信息级联预测算法进行实验对比,并讨论了一些信息级联预测算法中常用的算法实现细节。最后,总结了该领域未来可能的研究方向与发展趋势。

关键词: 级联增量预测, 节点预测, 流行度预测, 深度学习, 信息级联预测, 在线社交媒体

Abstract: 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.

Key words: Cascade size prediction, Deep learning, Information cascade prediction, Node prediction, Online social media, Popularity prediction

中图分类号:

TP183

张志扬, 张凤荔, 谭琪, 王瑞锦. 基于深度学习的信息级联预测方法综述[J]. 计算机科学, 2020, 47(7): 141-153. https://doi.org/10.11896/jsjkx.200300130

ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin. Review of Information Cascade Prediction Methods Based on Deep Learning[J]. Computer Science, 2020, 47(7): 141-153. https://doi.org/10.11896/jsjkx.200300130

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基于深度学习的信息级联预测方法综述

Review of Information Cascade Prediction Methods Based on Deep Learning

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