1. 武警工程大学电子技术系,陕西 西安 710086
2. 中国人民武装警察部队河北总队,河北 石家庄 050000
[ "张德阳(1993-),男,河南新乡人,武警工程大学硕士生,主要研究方向为舆情分析、社交网络。" ]
[ "韩益亮(1977-),男,甘肃会宁人,武警工程大学教授、博士生导师,主要研究方向为密码学、复杂网络分析。" ]
[ "李晓龙(1991-),男,河南安阳人,武警工程大学硕士生,主要研究方向为网络安全、舆情分析。" ]
[ "潘峰(1967-),男,北京人,武警工程大学副教授,主要研究方向为密码学、复杂网络分析。" ]
[ "周宜兴(1976-),男,河北沧州人,武警工程大学硕士生,主要研究方向为复杂网络分析。" ]
网络首发:2017-09,
纸质出版:2017-09-15
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张德阳, 韩益亮, 李晓龙, 等. 基于用户相对影响权重的热点事件传播阈值模型[J]. 网络与信息安全学报, 2017,3(9):24-30.
De-yang ZHANG, Yi-liang HAN, Xiao-long LI, et al. Threshold model for hot events spreading based on relative impact weight of the user[J]. Chinese Journal of Network and Information Security, 2017, 3(9): 24-30.
张德阳, 韩益亮, 李晓龙, 等. 基于用户相对影响权重的热点事件传播阈值模型[J]. 网络与信息安全学报, 2017,3(9):24-30. DOI: 10.11959/j.issn.2096-109x.2017.00195.
De-yang ZHANG, Yi-liang HAN, Xiao-long LI, et al. Threshold model for hot events spreading based on relative impact weight of the user[J]. Chinese Journal of Network and Information Security, 2017, 3(9): 24-30. DOI: 10.11959/j.issn.2096-109x.2017.00195.
结合现有的线性阈值模型,提出一种新的用户相对影响权重函数;结合信息自身敏感性以及节点自身的接受阈值特性,提出一种能衡量信息传播能力的阈值模型,即URLT模型。通过仿真实验模拟在不同网络、不同敏感度信息以及不同节点阈值的传播,并对最终传播情况进行对比,实验结果证明,其最终影响范围与真实传播情况符合。因此,该模型对信息传播规律的发现与抑制有一定的参考价值。
A new relative influence weight function was proposed in combination with the existing linear threshold model.Combining with the information itself sensitivity and the threshold characteristic of the node itself
a threshold model(URLT model) was proposed
which can measure the information communication ability.By simulating the spread of different networks
different sensitivity information and different node thresholds
and comparing the final propagation situation
the experimental results show that the final influence range is consistent with the real spread situation.Therefore
the model has some reference value for the discovery and suppression of the law of information dissemination.
LIU T , ZHONG Y , CHEN K . Interdisciplinary study on popularity prediction of social classified hot online events in China [J ] . Telematics and Informatics , 2017 , 34 ( 3 ): 755 - 764 .
JUNG S H , KIM J . A new way of extending network coverage:relay-assisted D2D communications in 3GPP [J ] . ICT Express , 2016 , 2 ( 3 ): 117 - 121 .
RASSHOTTE . Social influence:the blackwell encyclopedia of social psychology [M ] . Malden:Blackwell Publishing . 2007 .
ARAL S , WALKER D . Identifying influential and susceptible members of social networks [J ] .Sience.2012,337(6092):337-341. Sience , 2012 , 337 ( 6092 ): 337 - 341 .
吴信东 . 在线社交网络影响力分析 [J ] . 计算机学报 , 2015 , 37 ( 4 ): 735 - 751 .
WU X D . Analysis of the influence of online social networks [J ] . Journal of computer science , 2015 , 37 ( 4 ): 735 - 751 .
KEMPE D , KLEINBERG J , TARDOS E . Maximizing the spread of influence in a social network [C ] // The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2003 : 137 - 146 .
GRUHL D , GUHA R , LIBEN-NOWELL D , et al . Information diffusion though blogspace [C ] // The 13th International Conference on World Wide Web . 2004 : 491 - 501 .
CHEN W , YUAN Y , ZHANG L . Scalable influence maximization in social networks under the liner threshold model [C ] // The 2010 IEEE International Conference on Data Mining . 2010 : 88 - 97 .
CHEN W , WANG C , WANG Y . Scalable influence maximization for prevalent viral marketing in large-scale social networks [C ] // The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2010 : 1029 - 1038 .
TIAN J T , WANG Y T , FENG X J . A new hybrid algorithm for influence maximization in social networks [J ] . Chinese Journal of Computers , 2011 , 34 ( 10 ): 1956 - 1964 .
李弼程 , 戴锋 . 网络舆情分析——理论、技术与应用策略 [M ] . 北京:国防工业出版社 . 2015 .
LI B C , DAI F . Analysis of network public opinion:theory technology and application strategy [M ] . Beijing:National Defense Industry Press . 2015 .
WANG J L . An online social network information propagation model based on relative weight of users [J ] .Journal of Physics 2015,64(5). Journal of Physics , 2015 , 64 ( 5 ).
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