Computer Science ›› 2017, Vol. 44 ›› Issue (1): 71-74.doi: 10.11896/j.issn.1002-137X.2017.01.013

Previous Articles     Next Articles

Improved Linear Influence Diffusion Model Based on Users’ Distance

CAI Guo-yong and PEI Guang-zhan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The prediction of information diffusion based on historical behavior of users in on-line social network is one of the hot spots of current research.However,the traditional propagation models can only explain the diffusion regular pattern of information in social networks,and it cannot predict the dissemination of information.Since uninfected users will be affected by infected users,Jaewan Yang and Jwe Leskovec proposed a linear influence model (LIM),but LIM model only considers the time factor in the process of information dissemination,and it ignores the spatial information,namely the relationship of users.Therefore,firstly,a measure of users’ distance in social network was introduced in this paper.Combining the distance measure with LIM model,we proposed an improved LIM model based on distance regula-rization,namely d-LIM model.Through experiments on real data sets,the result shows that d-LIM model can achieve better prediction accuracy than the compared methods.

Key words: Social network,Influence,Diffusion prediction

[1] SAITO K,NAKANO R,KIMURA M.Prediction of information diffusion probabilities for independent cascade model[M]∥Knowledge-based Intelligent Information and Engineering Systems.Springer Berlin Heidelberg,2008:67-75.
[2] CHEN W,WANG C,WANG Y.Scalable influence maximization for prevalent viral marketing in large-scale social networks[C]∥Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2010:1029-1038.
[3] LI Dong,XU Zhi-ming,LI Sheng,et al.A survey on information diffusion in online social networks [J].Chinese Journal of Computers,2014,37(1):189-206.(in Chinese) 李栋,徐志明,李生,等.在线社会网络中信息扩散[J].计算机学报,2014,37(1):189-206.
[4] YANG J,COUNTS S.Predicting the Speed,Scale,and Range of Information Diffusion in Twitter[J].ICWSM,2010,10:355-358.
[5] WANG F,WANG H,XU K.Diffusive logistic model towards predicting information diffusion in online social networks[C]∥2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCSW).IEEE,2012:133-139.
[6] GUILLE A,HACID H.A predictive model for the temporal dynamics of information diffusion in online social networks[C]∥Proceedings of the 21st International Conference Companion on World Wide Web.ACM,2012:1145-1152.
[7] 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.ACM,2014:393-402.
[8] WANG Y,XIANG G,CHANG S K.Sparse Multi-Task Lear-ning for Detecting Influential Nodes in an Implicit Diffusion Network[C]∥AAAI.2013.
[9] LIN Y,RAZA A A,LEE J Y,et al.Influence propagation:patterns,model and a case study[M]∥Advances in Knowledge Discovery and Data Mining.Springer International Publishing,2014:386-397.
[10] WANG F,WANG H,XU K,et al.Characterizing information diffusion in online social networks with linear diffusive model[C]∥2013 IEEE 33rd International Conference on Distributed Computing Systems (ICDCS).IEEE,2013:307-316.
[11] YANG J,LESKOVEC J.Modeling information diffusion in implicit networks[C]∥2010 IEEE 10th International Conference on Data Mining (ICDM).IEEE,2010:599-608.
[12] MA H,ZHOU D,LIU C,et al.Recommender systems with social regularization[C]∥Proceedings of the Fourth ACM International Conference on Web Search and Data Mining.ACM,2011:287-296.

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] 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 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] 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 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] 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, 116 .