计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 118-123.doi: 10.11896/jsjkx.200600155
桑春艳1, 胥文1, 贾朝龙1, 文俊浩2
SANG Chun-yan1, XU Wen1, JIA Chao-long1, WEN Jun-hao2
摘要： 与传统媒体相比,社交网络在传播新闻、思想、观点等方面发挥着突出的作用,同时也是传播谣言、虚假新闻等负面信息的最佳途径。因此,对网络舆情演化趋势的准确预测和有效控制已成为重要的研究话题。目前,大多数研究从理论建模的角度对网络舆情事件的演化特性和发展趋势进行预测,基于用户行为特征的信息传播演化趋势预测模型的建模及分析有待进一步研究。考虑到信息传播过程中用户之间的相互影响,文中提出一种基于注意力机制的方法,旨在探究社交网络中用户在信息传播过程中的影响来预测信息的传播趋势。首先,利用基于长短时记忆神经网络(Long Shot-Term Memory,LSTM)的网络架构来获取信息传播的轨迹特征。其次,考虑到信息传播和用户行为的复杂性,利用注意力机制挖掘用户之间的依赖性来预测真实的信息传播过程。最后,综合考虑影响信息传播的驱动因素,得到一种基于注意力机制的信息传播演化趋势预测模型(Attention Diffusion Neural Network,ADNN)。在4个对比数据集上的实验结果显示,ADNN模型优于流行的序列模型,该模型能够有效利用驱动因素对信息传播的影响,更准确地预测社交网络中信息的传播趋势。
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| [1]STAI E,MILAIOU E,KARYOTIS V,et al.Temporal dynamics of information diffusion in twitter:Modeling and experimentation[J].IEEE Transactions on Computational Social Systems,2018,5(1):256-264. [2]KUHNLE A,ALIM M A,LI X,et al.Multiplex influence maximization in online social networks with heterogeneous diffusion models[J].IEEE Transactions on Computational Social Systems,2018,5(2):418-429. [3]MATSUBARA Y,SAKURAI Y,PRAKASH B A,et al.Nonli-near dynamics of information diffusion in social networks[J].ACM Transactions on the Web (TWEB),2017,11(2):1-40. [4]JIANG Y,JIANG J C.Understanding social networks from a multiagent perspective[J].IEEE Transactions on Parallel and Distributed Systems,2013,25(10):2743-2759. [5]XIONG F,WANG X M,CHENG J J.Subtle role of latency for information diffusion in online social neworks [J].Chinese Physics B,2016,25(10):108904. [6]LAGNIER C,DENOYER L,GAUSSIER E,et al.Predicting information diffusion in social networks using content and user’s profiles[C]//European Conference on Information Retrieval.Berlin,Heidelberg:Springer,2013:74-85. [7]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.2018:1795-1798. [8]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. [9]BOURIGAULT S,LAGNIER C,LAMPRIER S,et al.Learning social network embeddings for predicting information diffusion[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining.2014:393-402. [10]SUBBIAN K,PRAKASH B A,ADAMIC L.Detecting large reshare cascades in social networks[C]//Proceedings of the 26th International Conference on World Wide Web.2017:597-605. [11]ANDERSON P,HE X,BUEHLER C,et al.Bottom-up and top-down attention for image captioning and visual question answe-ring[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:6077-6086. [12]WANG F,JIANG M,QIAN C,et al.Residual attention network for image classification[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2017:3156-3164. [13]LUONG M T,PHAM H,MANNING C D.Effective approaches to attention-based neural machine translation[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1412-1421. [14]PARK S H,KIM B D,KANG C M,et al.Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture[C]//2018 IEEE Intelligent Vehicles Symposium (IV).IEEE,2018:1672-1678. [15]GASKÓN,KÉPES T,SUCIU M A,et al.Considerations about using the Shapley Value for Influence Maximization in the case of the Weighted Cascade Model[C]//2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI).IEEE,2020:257-262. [16]BOZORGI A,SAMET S,KWISTHOUT J,et al.Community-based influence maximization in social networks under a compe-titive linear threshold model[J].Knowledge-Based Systems,2017,134:149-158. [17]GOMEZ-RODRIGUEZ M,BALDUZZI D,SCHÖLKOPF B.Uncovering the temporal dynamics of diffusion networks[C]// Proceedings of the 28th International Conference on Internatio-nal Conference on Machine Learning.2011:561-568. [18]GOMEZ-RODRIGUEZ M,LESKOVEC J,KRAUSE A.Infer-ring networks of diffusion and influence[J].ACM Transactions on Knowledge Discovery from Data (TKDD),2012,5(4):1-37. [19]BOURIGAULT S,LAMPRIER S,GALLINARI P.Representation learning for information diffusion through social networks:an embedded cascade model[C]//Proceedings of the Ninth ACM international conference on Web Search and Data Mining.2016:573-582. [20]SAGHEER A,KOTB M.Time series forecasting of petroleum production using deep LSTM recurrent networks[J].Neurocomputing,2019,323:203-213. [21]WANG H,ZHANG F,ZHANG M,et al.Knowledge-awaregraph neural networks with label smoothness regularization for recommender systems[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:968-977. [22]WANG Z,LI W.Hierarchical Diffusion Attention Network[C]//IJCAI.2019:3828-3834. [23]YANG C,SUN M,LIU H,et al.Neural Diffusion Model for Microscopic Cascade Study[J].IEEE Transactions on Knowledge and Data Engineering,2021,33(3):1128-1139. [24]OPSAHL T,PANZARASA P.Clustering in weighted networks[J].Social networks,2009,31(2):155-163. [25]DU N,DAI H,TRIVEDI R,et al.Recurrent marked temporal point processes:Embedding event history to vector[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:1555-1564. [26]HU W,SINGH K K,XIAO F,et al.Who will share my image? Predicting the content diffusion path in online social networks[C]//Proceedings of the Eleventh ACM International Confe-rence on Web Search and Data Mining.2018:252-260. [27]DEY R,SALEMT F M.Gate-variants of gated recurrent unit(GRU) neural networks[C]//2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).IEEE,2017:1597-1600. [28]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008. |
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