Computer Science ›› 2020, Vol. 47 ›› Issue (7): 141-153.doi: 10.11896/jsjkx.200300130

• Artificial Intelligence • Previous Articles     Next Articles

Review of Information Cascade Prediction Methods Based on Deep Learning

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

  1. 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)

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

CLC Number: 

  • TP183
[1] YANG J,COUNTS S.Predicting the speed,scale,and range of information diffusion in twitter[C]//Fourth International AAAI Conference on Weblogs and Social Media.2010.
[2] GRUHL D,GUHA R,LIBEN-NOWELL D,et al.Information diffusion through blogspace[C]//Proceedings of the 13th international conference on World Wide Web.ACM,2004:491-501.
[3] LESKOVEC J,MCGLOHON M,FALOUTSOS C,et al.Patterns of cascading behavior in large blog graphs[C]//Procee-dings of the 2007 SIAM international conference on data mining.Society for Industrial and Applied Mathematics,2007:551-556.
[4] GOLUB B,JACKSON M O.Using selection bias to explain the observed structure of internet diffusions[J].Proceedings of the National Academy of Sciences,2010,107(24):10833-10836.
[5] LIBEN-NOWELL D,KLEINBERG J.Tracing information flow on a global scale using Internet chain-letter data[J].Proceedings of the National Academy of Sciences of the United States of America,2008,105(12):4633-4638.
[6] LESKOVEC J,ADAMIC L A,HUBERMAN B A.The dyna-mics of viral marketing[J].ACM Transactions on the Web (TWEB),2007,1(1):5.
[7] DOW P A,ADAMIC L A,FRIGGERI A.The anatomy of large facebook cascades[C]//Seventh international AAAI conference on weblogs and social media.2013.
[8] KUMAR R,MAHDIAN M,MCGLOHON M.Dynamics of conversations[C]//Proceedings of the 16th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.ACM,2010:553-562.
[9] QIU J,TANG J,MA H,et al.Deepinf:Social influence prediction with deep learning[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2018:2110-2119.
[10] GE Y,CHEN S C.Graph Convolutional Network for Recom-mender Systems[J/OL].[2020-02-20].http://www.jos.org.cn/1000-9825/5928.htm.
[11] KUPAVSKII A,OSTROUMOVA L,UMNOV A,et al.Prediction of retweet cascade size over time[C]//Proceedings of the 21st ACMInternational Conference on Information and Know-ledge Management.ACM,2012:2335-2338.
[12] MA Z,SUN A,CONG G.On predicting the popularity of newly emerging hashtags in Twitter[J].Journal of the American So-ciety for Information Science and Technology,2013,64(7):1399-1410.
[13] PETROVIC S,OSBORNE M,LAVRENKO V.Rt to win! predicting message propagation in twitter[C]//Fifth International AAAI Conference on Weblogs and Social Media.2011.
[14] SZABO G,HUBERMAN B A.Predicting the popularity of online content[J].Communications of the ACM,2010,53(8):80-88.
[15] GUO R,SHAKARIAN P.A comparison of methods for cascade prediction[C]//Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.IEEE Press,2016:591-598.
[16] CHENG J,ADAMIC L,DOW P A,et al.Can cascades be predicted?[C]//Proceedings of the 23rdInternational Conference on World Wide Web.ACM,2014:925-936.
[17] WENG L,MENCZER F,AHN Y Y.Virality prediction andcommunity structure in social networks[J].Scientific reports,2013,3:2522.
[18] TSUR O,RAPPOPORT A.What's in a hashtag?:content based prediction of the spread of ideas in microblogging communities[C]//Proceedings of the fifth ACMInternational Conference on Web Search and Data Mining.ACM,2012:643-652.
[19] BAKSHY E,HOFMAN J M,MASON W A,et al.Everyone’s an influencer:quantifying influence on twitter[C]//Proceedings of the4th ACM International Conference on Web Search and Data Mining.ACM,2011:65-74.
[20] MARTIN T,HOFMAN J M,SHARMA A,et al.Exploring limits to prediction in complex social systems[C]//Proceedings of the 25th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2016:683-694.
[21] BAO P,SHEN H W,JIN X,et al.Modeling and predicting po-pularity dynamics of microblogs using self-excited hawkes processes[C]//Proceedings of the 24th International Conference on World Wide Web.ACM,2015:9-10.
[22] ZHAO Q,ERDOGDU M A,HE H Y,et al.Seismic:A self-exciting point process model for predicting tweet popularity[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:1513-1522.
[23] CRANE R,SORNETTE D.Robust dynamic classes revealed by measuring the response function of a social system[J].Proceedings of the National Academy of Sciences of the United States of America,2008,105(41):15649-15653.
[24] RIZOIU M A,XIE L,SANNER S,et al.Expecting to be hip:Hawkes intensity processes for social media popularity[C]//Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:735-744.
[25] XIAO S,YAN J,YANG X,et al.Modeling the intensity function of point process via recurrent neural networks[C]//Thirty-First AAAI Conference on Artificial Intelligence.2017.
[26] WANG Y,SHEN H,LIU S,et al.Cascade Dynamics Modeling with Attention-based Recurrent Neural Network[C]//IJCAI.2017:2985-2991.
[27] SHEN H,WANG D,SONG C,et al.Modeling and predictingpopularity dynamics via reinforced poisson processes[C]//Twenty-eighth AAAIConference on Artificial Intelligence.2014.
[28] GAO J,SHEN H,LIU S,et al.Modeling and predicting retweeting dynamics via a mixture process[C]//Proceedings of the 25th International Conference Companion on World Wide Web.International World Wide Web Conferences Steering Committee,2016:33-34.
[29] GAO S,MA J,CHEN Z.Modeling and predicting retweeting dynamics on microblogging platforms[C]//Proceedings of the Eighth ACM International Conference on Web Search and Data Mining.ACM,2015:107-116.
[30] GOMEZ-RODRIGUEZ M,LESKOVEC J,SCHÖLKOPF B.Modeling information propagation with survival theory[C]//International Conference on Machine Learning.2013:666-674.
[31] KEMPE D,KLEINBERG J,TARDOS É.Maximizing the spread of influence through a social network[C]//Proceedings of the9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2003:137-146.
[32] WANG Y,SHEN H W,LIU S,et al.Learning user-specific latent influence and susceptibility from information cascades[J].arXiv preprint arXiv:1310.3911,2013.
[33] LI C,MA J,GUO X,et al.Deepcas:An end-to-end predictor of information cascades[C]//Proceedings of the 26thInternational Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:577-586.
[34] LIN J,ZHANG L,HE M,et al.Multi-path relationship pre-served social network embedding[J].IEEE Access,2019,7:26507-26518.
[35] CAO Q,SHEN H,CEN K,et al.Deephawkes:Bridging the gap between prediction and understanding of information cascades[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.ACM,2017:1149-1158.
[36] WANG S J,ZHOU L H,KONG B,et al.LDA-DeepHawkesmodel for predicting information cascade[J/OL].Journal of Frontiers of Computer Science and Technology:1-21.[2019-10-14].http://kns.cnki.net/kcms/detail/11.5602.TP.20190628.1726.006.html.
[37] ISLAM M R,MUTHIAH S,ADHIKARI B,et al.DeepDiffuse:Predicting the'Who'and'When'in Cascades[C]//2018 IEEE International Conference on Data Mining (ICDM).IEEE,2018:1055-1060.
[38] PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.ACM,2014:701-710.
[39] RAMAGE D,DUMAIS S,LIEBLING D.Characterizing microblogs with topic models[C]//4th International AAAI Conference on Weblogs and Social Media.2010.
[40] ZHANG C Y,SUN J L,DING Y Q.Topic mining for microblog based on MB-LDA model[J].Journal of Computer Research and Development,2011,48(10):1795-1802.
[41] KANG J H,LERMAN K,GETOOR L.LA-LDA:a limited attention topic model for social recommendation[C]//Internatio-nal Conference on Social Computing,Behavioral-Cultural Mode-ling,and Prediction.Heidelberg:Springer 2013:211-220.
[42] LIU Y,WANG J,JIANG Y.PT-LDA:A latent variable model to predict personality traits of social network users[J].Neurocomputing,2016,210:155-163.
[43] NI L P,LIU X J,MA C Y.Topic Evolution Analysis Based on LDA Model and AP Clustering[J].Computer Technology and Development,2016,26(12):6-11.
[44] CHEN G,KONG Q,XU N,et al.NPP:A neural popularity prediction model for social media content[J].Neurocomputing,2019,333:221-230.
[45] WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].arXiv:1901.00596,2019.
[46] BENGIO Y,DUCHARME R,VINCENT P,et al.A neuralprobabilistic language model[J].Journal of machine learning research,2003,3(Feb):1137-1155.
[47] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[C]//Advances inNeural Information Processing Systems.2012:1097-1105.
[48] NIEPERT M,AHMED M,KUTZKOV K.Learning convolu-tional neural networks for graphs[C]//InternationalConference on Machine Learning.2016:2014-2023.
[49] DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances inNeural Information Processing Systems.2016:3844-3852.
[50] LI C,GUO X,MEI Q.Deepgraph:Graph structure predicts network growth[J].arXiv preprint arXiv:1610.06251,2016.
[51] WANG Z,CHEN C,LI W.A Sequential Neural InformationDiffusion Model with Structure Attention[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.ACM,2018:1795-1798.
[52] WANG J,ZHENG V W,LIU Z,et al.Topological recurrentneural network for diffusion prediction[C]//2017 IEEE International Conference on Data Mining (ICDM).IEEE,2017:475-484.
[53] CHEN X,ZHOU F,ZHANG K,et al.Information DiffusionPrediction via Recurrent Cascades Convolution[C]//2019 IEEE 35th International Conference on Data Engineering (ICDE).IEEE,2019:770-781.
[54] CHEN X,ZHANG K,ZHOU F,et al.Information CascadesModeling via Deep Multi-Task Learning[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2019:885-888.
[55] YANG C,TANG J,SUN M,et al.Multi-scale information diffusion prediction with reinforced recurrent networks[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.AAAI Press,2019:4033-4039.
[56] SHUAI B,ZUO Z,WANG B,et al.Dag-recurrent neural net-works for scene labeling[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:3620-3629.
[57] BALDI P,POLLASTRI G.The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem[J].Journal of Machine Learning Research,2003,4(Sep):575-602.
[58] BIANCHINI M,MAGGINI M,SARTI L,et al.Recursive neural networks for processing graphs with labelled edges:Theory and applications[J].Neural Networks,2005,18(8):1040-1050.
[59] LEI T,ZHANG Y,ARTZI Y.Training rnns as fast as cnns[J].arXiv:1709.02755,2017.
[60] HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs[C]//Advances in Neural Information Processing Systems.2017:1024-1034.
[61] KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[62] VELIKOVI P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv preprint arXiv:1710.10903,2017.
[63] LI Y,ZHANG Z L.Digraph laplacian and the degree of asymmetry[J].Internet Mathematics,2012,8(4):381-401.
[64] VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[65] SUTTON R S,MCALLESTER D A,SINGH S P,et al.Policy gradient methods for reinforcement learning with function approximation[C]//Advances in Neural Information Processing Systems.2000:1057-1063.
[66] HODAS N O,LERMAN K.The simple rules of social contagion[J].Scientific Reports,2014,4:4343.
[67] GEHRKE J,GINSPARG P,KLEINBERG J.Overview of the2003 KDD Cup[J].Acm SIGKDD Explorations Newsletter,2003,5(2):149-151.
[68] LESKOVEC J,BACKSTROM L,KLEINBERG J.Meme-tracking and the dynamics of the news cycle[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2009:497-506.
[69] HOGG T,LERMAN K.Social dynamics of digg[J].EPJ Data Science,2012,1(1):5.
[70] TANG L,LIU H.Relational learning via latent social dimensions[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2009:817-826.
[71] BUCKLEY C,VOORHEES E M.Retrieval evaluation with incomplete information[C]//Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2004:25-32.
[72] WANG Z,LI W.Hierarchical diffusion attention network[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.AAAI Press,2019:3828-3834.
[73] SUN S.Multi-view Laplacian support vector machines[C]//International Conference on Advanced Data Mining and Applications.Heidelberg:Springer,2011:209-222.
[74] SUN S,SHAWE-TAYLOR J,MAO L.PAC-Bayes analysis of multi-view learning[J].Information Fusion,2017,35:117-131.
[75] JING P,SU Y,NIE L,et al.Low-rank multi-view embedding learning for micro-video popularity prediction[J].IEEE Transactions on Knowledge and Data Engineering,2017,30(8):1519-1532.
[76] GONG W H,CHEN Y Q,PEI X B,et al.Community detection combined with multi-dimensional relationships in location-based social networks[J/OL].Journal of Software:1-16.[2019-10-08].http://jos.org.cn/1000-9825/5269.htm.
[77] BODIN E,MALIK I,EK C H,et al.Nonparametric inference for auto-encoding variational Bayes[J].arXiv:1712.06536,2017.
[78] MASCI J,MEIER U,CIREŞAN D,et al.Stacked convolutional auto-encoders for hierarchical feature extraction[C]//International Conference on Artificial Neural Networks.Heidelberg:Springer,2011:52-59.
[79] CHEN X,DUAN Y,HOUTHOOFT R,et al.Infogan:Inter-pretable representation learning by information maximizing generative adversarial nets[C]//Advances in Neural Information Processing Systems.2016:2172-2180.
[80] GANIN Y,USTINOVA E,AJAKAN H,et al.Domain-adversarial training of neural networks[J].The Journal of Machine Learning Research,2016,17(1):2096-2030.
[81] REZAEI A,XIAO C,GAO J,et al.Protecting Sensitive Attri-butes via Generative Adversarial Networks[J].arXiv:1812.10193,2018.
[82] ZHOU F,GAO Q,TRAJCEVSKI G,et al.Trajectory-UserLinking via Variational AutoEncoder[C]//IJCAI.2018:3212-3218.
[83] DIAO W,SUN X,DOU F,et al.Object recognition in remotesensing images using sparse deep belief networks[J].Remote Sensing Letters,2015,6(10):745-754.
[84] CUI Z,CAO Z,YANG J,et al.Hierarchical recognition system for target recognition from sparse representations[J].Mathematical Problems in Engineering,2015.
[1] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[2] YU Wen-jia, DING Shi-fei. Conditional Generative Adversarial Network Based on Self-attention Mechanism [J]. Computer Science, 2021, 48(1): 241-246.
[3] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[4] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[5] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[6] YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[7] DENG Liang, XU Geng-lin, LI Meng-jie, CHEN Zhang-jin. Fast Face Recognition Based on Deep Learning and Multiple Hash Similarity Weighting [J]. Computer Science, 2020, 47(9): 163-168.
[8] BAO Yu-xuan, LU Tian-liang, DU Yan-hui. Overview of Deepfake Video Detection Technology [J]. Computer Science, 2020, 47(9): 283-292.
[9] YUAN Ye, HE Xiao-ge, ZHU Ding-kun, WANG Fu-lee, XIE Hao-ran, WANG Jun, WEI Ming-qiang, GUO Yan-wen. Survey of Visual Image Saliency Detection [J]. Computer Science, 2020, 47(7): 84-91.
[10] WANG Wen-dao, WANG Run-ze, WEI Xin-lei, QI Yun-liang, MA Yi-de. Automatic Recognition of ECG Based on Stacked Bidirectional LSTM [J]. Computer Science, 2020, 47(7): 118-124.
[11] LIU Yan, WEN Jing. Complex Scene Text Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(7): 135-140.
[12] JIANG Wen-bin, FU Zhi, PENG Jing, ZHU Jian. 4Bit-based Gradient Compression Method for Distributed Deep Learning System [J]. Computer Science, 2020, 47(7): 220-226.
[13] CHEN Jin-yin, ZHANG Dun-Jie, LIN Xiang, XU Xiao-dong and ZHU Zi-ling. False Message Propagation Suppression Based on Influence Maximization [J]. Computer Science, 2020, 47(6A): 17-23.
[14] CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu. Improving Hi-C Data Resolution with Deep Convolutional Neural Networks [J]. Computer Science, 2020, 47(6A): 70-74.
[15] HE Lei, SHAO Zhan-peng, ZHANG Jian-hua and ZHOU Xiao-long. Review of Deep Learning-based Action Recognition Algorithms [J]. Computer Science, 2020, 47(6A): 139-147.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .