Learning sequential features for cascade outbreak prediction

doi:10.1007/s10115-017-1143-0

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Learning sequential features for cascade outbreak prediction
	Gou, Chengcheng 1,2; Shen, Huawei 1,2; Du, Pan 2; Wu, Dayong 2; Liu, Yue 2; Cheng, Xueqi 1,2
刊名	KNOWLEDGE AND INFORMATION SYSTEMS
	2018-12-01
卷号	57 期号:3 页码:721-739
关键词	Social network Outbreak prediction Sequential feature LSTM Popularity prediction
ISSN号	0219-1377
DOI	10.1007/s10115-017-1143-0
英文摘要	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.
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
语种	英语
出版者	SPRINGER LONDON LTD
WOS记录号	WOS:000443972500009
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4902]
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.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, 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.