Computer Science ›› 2020, Vol. 47 ›› Issue (6): 201-209.doi: 10.11896/jsjkx.200200117

• Artificial Intelligence • Previous Articles     Next Articles

Information Cascade Prediction Model Based on Hierarchical Attention

ZHANG Zhi-yang, ZHANG Feng-li, CHEN Xue-qin, WANG Rui-jin   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu610054,China
  • Received:2020-02-26 Online:2020-06-15 Published:2020-06-10
  • 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),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: Information cascade prediction is a research hotspot in the field of social network analysis.It learns the propagation mode of information in online social media through the diffusion sequence and topology map of the information cascade.Most current models for solving this task are based on recurrent neural networks and only consider information cascading time series structure information or spatial structure information inside sequences,and cannot learn topological relationships between sequences.And the existing cascade graph structure learning methods cannot assign different weights to the neighbors of the nodes,resulting in poor association learning between the nodes.In response to the above problems,this paper proposes an information cascade sampling method based on node representation,which models the information cascade as a node representation rather than a sequence representation.This paper also proposes an information cascade prediction model based on hierarchical attention network (ICPHA),which learns the time series structure information of the node sequence through a recurrent neural network layer with self-attention mechanism,and learns the spatial structure information between node representations through a multi-head attention mechanism.By this way,ICPHA jointly models the structural information of the information cascade through a hierarchical attention network.ICPHA has achieved leading prediction results on Twitter,Memes,and Digg,and has good generalization ability.

Key words: Online social media, Deep learning, Recurrent neural network, Graph representation learning, Information cascade prediction, Multi-head attention mechanism

CLC Number: 

  • TP183
[1]ZHU X,JIA Y,NIE Y P,et al.Event Propagation Analysis on Microblog[J].Journal of Computer Research and Development,2015,52(2):437-444.
[2]CHENG J,ADAMIC L,DOW P A,et al.Can cascades be predicted?[C]//Proceedings of the 23rd International Conference on World Wide Web.ACM,2014:925-936.
[3]JIANG Y,COUNTS S.Predicting the speed,scale,and range of information diffusion in twitter[C]//Fourth International AAAI Conference on Weblogs and Social Media.2010.
[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]LESKOVEC J.The Dynamics of Viral Marketing[J].Acm Transactions on the Web,2005,1(1):228-237.
[6]DOW A P,ADAMIC L A,FRIGGERI A.The Anatomy of Large Facebook Cascades[C]//ICWSM.2013.
[7]KUMAR R,MAHDIAN M,MCGLOHON M.Dynamics of conversations[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2010:553-562.
[8]CHENG L,MA J Q,GUO X X,et al.Deepcas:An end-to-end predictor of information cascades[C]//Proceedings of the 26th international conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:577-586.
[9]WANG X S,MA S Z.Method of Weibo User Influence Calculation Integrating Users’ Own Factors and Interaction Behavior[J].Computer Science,2020,47(1):96-101.
[10]BENGIO Y,DUCHARME R,VINCENT P,et al.A neural probabilistic language model[J].Journal of Machine Learning Research,2003,3(Feb):1137-1155.
[11]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.2012:1097-1105.
[12]QIU J Z,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.
[13]VELIKOVI P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017.
[14]GUO R C,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,2016:591-598.
[15]BAO P,SHEN H W,JIN X L,et al.Modeling and predicting popularity dynamics of microblogs using self-excited hawkes processes[C]//Proceedings of the 24th International Conference on World Wide Web.ACM,2015:9-10.
[16]KEMPE D,KLEINBERG J,TARDOS É.Maximizing the spread of influence through a social network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2003:137-146.
[17]CAO Q,SHEN H W,CEN K T,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.
[18]CHEN X Q,ZHOU F,ZHANG K P,et al.Information Diffusion Prediction via Recurrent Cascades Convolution[C]//2019 IEEE 35th International Conference on Data Engineering (ICDE).IEEE,2019:770-781.
[19]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[20]TONG H H,FALOUTSOS C,PAN J Y.Fast random walk with restart and its applications[C]//Sixth International Conference on Data Mining (ICDM’06).IEEE,2006:613-622.
[21]MNIH V,HEESS N,GRAVES A.Recurrent models of visual attention[C]//Advances in Neural Information Processing Systems.2014:2204-2212.
[22]WANG Z T,CHEN C Y,LI W J.A Sequential Neural Information Diffusion Model with Structure Attention[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.ACM,2018:1795-1798.
[23]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.
[24]GAO S,MA J,CHEN Z M.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.
[25]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.
[26]TAD H,KRISTINA L.Social Dynamics of Digg[C]//Proceedings of the Fourth International Conference on Weblogs and Social Media(ICWSM 2010).Washington,DC,USA,2010:23-26.
[27]WANG J,ZHENG V,LIU Z M,et al.Topological recurrentneural network for diffusion prediction[C]//2017 IEEE International Conference on Data Mining (ICDM).IEEE,2017:475-484.
[1] WANG Yan, WANG Li. Local Gabor Convolutional Neural Network for Hyperspectral Image Classification [J]. Computer Science, 2020, 47(6): 151-156.
[2] PEI Jia-zhen, XU Zeng-chun, HU Ping. Person Re -identification Fusing Viewpoint Mechanism and Pose Estimation [J]. Computer Science, 2020, 47(6): 164-169.
[3] JING Yu, QI Rui-hua, LIU Jian-xin, LIU Zhao-xia. Gesture Recognition Algorithm Based on Improved Multiscale Deep Convolutional Neural Network [J]. Computer Science, 2020, 47(6): 180-183.
[4] XIONG Ting, QI Yong, ZHANG Wei-bin. Short-term Traffic Flow Prediction Based on DCGRU-RF Model for Road Network [J]. Computer Science, 2020, 47(5): 84-89.
[5] WANG Hang, CHEN Xiao, TIAN Sheng-zhao, CHEN Duan-bing. SAR Image Recognition Based on Few-shot Learning [J]. Computer Science, 2020, 47(5): 124-128.
[6] CAI Qiang, DENG Yi-biao, LI Hai-sheng, YU Le, MING Shao-feng. Survey on Human Action Recognition Based on Deep Learning [J]. Computer Science, 2020, 47(4): 85-93.
[7] ZHANG Peng, SONG Yi-fan, ZONG Li-bo, LIU Li-bo. Advances in 3D Object Detection:A Brief Survey [J]. Computer Science, 2020, 47(4): 94-102.
[8] HU Chao-wen, YANG Ya-lian, WU Chang-xing. Survey of Implicit Discourse Relation Recognition Based on Deep Learning [J]. Computer Science, 2020, 47(4): 157-163.
[9] LIU Yan, LEI Yin-jie, NING Qian. Study of Crowd Counting Algorithm of “Weak Supervision” Dense Scene Based on DeepNeural Network [J]. Computer Science, 2020, 47(4): 184-188.
[10] YU Shan-shan, SU Jin-dian, LI Peng-fei. Sentiment Classification Method for Sentences via Self-attention [J]. Computer Science, 2020, 47(4): 204-210.
[11] LI Tai-song,HE Ze-yu,WANG Bing,YAN Yong-hong,TANG Xiang-hong. Session-based Recommendation Algorithm Based on Recurrent Temporal Convolutional Network [J]. Computer Science, 2020, 47(3): 103-109.
[12] CHEN Li-fu,LIU Yan-zhi,ZHANG Peng,YUAN Zhi-hui,XING Xue-min. Road Extraction Algorithm of Multi-feature High-resolution SAR Image Based on Multi-Path RefineNet [J]. Computer Science, 2020, 47(3): 156-161.
[13] ANG Wei-yi,BAI Chen-jia,CAI Chao,ZHAO Ying-nan,LIU Peng. Survey on Sparse Reward in Deep Reinforcement Learning [J]. Computer Science, 2020, 47(3): 182-191.
[14] LIU Xiao-ling,LIU Bai-song,WANG Yang-yang,TANG Hao. Research and Development of Multi-label Generation Based on Deep Learning [J]. Computer Science, 2020, 47(3): 192-199.
[15] HUANG Hong-wei,LIU Yu-jiao,SHEN Zhuo-kai,ZHANG Shao-wei,CHEN Zhi-min,GAO Yang. End-to-end Track Association Based on Deep Learning Network Model [J]. Computer Science, 2020, 47(3): 200-205.
Full text



[1] . [J]. Computer Science, 2020, 47(5): 2 .
[2] KONG Fang, LI Qi-zhi, LI Shuai. Survey on Online Influence Maximization[J]. Computer Science, 2020, 47(5): 7 -13 .
[3] . Contents[J]. Computer Science, 2020, 47(5): 0 .
[4] ZHOU Heng, WANG Yong-jun, WANG Bao-shan, YAN Jian. Deeper Explanation of Quantum Logic in Intuitionistic Perspective[J]. Computer Science, 2020, 47(5): 1 -6 .
[5] . [J]. Computer Science, 2020, 47(6): 0 .
[6] WANG Hui-yan, XU Jing-wei, XU Chang. Survey on Runtime Input Validation for Context-aware Adaptive Software[J]. Computer Science, 2020, 47(6): 1 -7 .
[7] LI Ling, LI Huang-hua, WANG Sheng-yuan. Experiment on Formal Verification Process of Parser of CompCert Compiler in Trusted Compiler Design[J]. Computer Science, 2020, 47(6): 8 -15 .
[8] ZHAO Song-hui, REN Zhi-lei, JIANG He. Multi-objective Optimization Methods for Software Upgradeability Problem[J]. Computer Science, 2020, 47(6): 16 -23 .
[9] CUI Kai, ZHAO Guo-liang, ZHOU Kuan-jiu, LI Ming-chu. Model of Embedded Software for Solving Concurrent Defects[J]. Computer Science, 2020, 47(6): 24 -31 .
[10] XU Zi-xi, MAO Xin-jun, YANG Yi, LU Yao. Modeling and Simulation of Q&A Community and Its Incentive Mechanism[J]. Computer Science, 2020, 47(6): 32 -37 .