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Learning sequential features for cascade outbreak prediction

文献类型：期刊论文

作者Gou, Chengcheng1,2; Shen, Huawei1,2; Du, Pan2; Wu, Dayong2; Liu, Yue2; Cheng, Xueqi1,2
刊名KNOWLEDGE AND INFORMATION SYSTEMS
出版日期2018-12-01
卷号57期号:3页码:721-739
ISSN号0219-1377
关键词Social network Outbreak prediction Sequential feature LSTM Popularity prediction
DOI10.1007/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
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.

入库方式： OAI收割

来源：计算技术研究所

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