Learning sequential features for cascade outbreak prediction

doi:10.1007/s10115-017-1143-0

	Learning sequential features for cascade outbreak prediction
	Gou, Chengcheng 1,2; Shen, Huawei 1,2; Du, Pan 1; Wu, Dayong 1; Liu, Yue 1; Cheng, Xueqi 1,2
	2018-12-01
发表期刊	KNOWLEDGE AND INFORMATION SYSTEMS
ISSN	0219-1377
卷号	57 期号:3 页码:721-739
摘要	Information cascades are ubiquitous in various online social networks. Outbreak of cascades could cause huge and unexpected effects. Therefore, predicting the outbreak of cascades at early stage is of vital importance to avoid potential bad effects and take relevant actions. Existing methods either adopt regression or classification technique with exhaustive feature engineering or predict cascade dynamics via modeling the stochastic process of cascades using a hard-coded diffusion-reaction function. One salient issue of these methods is that these methods heavily depend on human-defined knowledge, features or functions. In this paper, we propose to use recurrent neural network with long short-term memory to directly learn sequential patterns from information cascades, working in a fully data-driven manner. With the learned sequential patterns, the outbreak of cascade could be accurately predicted. Extensive experiments on both Twitter and Sina Weibo datasets demonstrate that our method significantly outperforms state-of-the-art methods at the prediction of cascade outbreaks.
关键词	Social network Outbreak prediction Sequential feature LSTM Popularity prediction
DOI	10.1007/s10115-017-1143-0
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973 Program)[2014CB340401] ; National Basic Research Program of China (973 Program)[2013CB329602] ; National Natural Science Foundation of China[61425016] ; National Natural Science Foundation of China[61472400] ; National Natural Science Foundation of China[61572467] ; National Natural Science Foundation of China[61433014] ; National High Technology Research and Development Program of China (863 Program)[2014AA015204] ; National High Technology Research and Development Program of China (863 Program)[2015AA015803] ; Key Technologies RD Program[2017YFB0803302] ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000443972500009
出版者	SPRINGER LONDON LTD
引用统计	被引频次：30[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4903
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gou, Chengcheng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Gou, Chengcheng,Shen, Huawei,Du, Pan,et al. Learning sequential features for cascade outbreak prediction[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2018,57(3):721-739.
APA	Gou, Chengcheng,Shen, Huawei,Du, Pan,Wu, Dayong,Liu, Yue,&Cheng, Xueqi.(2018).Learning sequential features for cascade outbreak prediction.KNOWLEDGE AND INFORMATION SYSTEMS,57(3),721-739.
MLA	Gou, Chengcheng,et al."Learning sequential features for cascade outbreak prediction".KNOWLEDGE AND INFORMATION SYSTEMS 57.3(2018):721-739.