在线社交网络多社区免疫的谣言抑制策略
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摘要
在线社交网络谣言传播的中后期,谣言抑制策略的效果不明显。以传染病传播模型建立在线社交网络的谣言传播模型,提出了一种多社区免疫的谣言抑制策略,首先识别出免疫节点在社交网络中所处的一个或多个社区,然后对每个社区的关键节点以一定概率实施目标免疫。以Facebook好友关系为实验数据进行仿真实验,实施多社区免疫的谣言抑制策略,并与其他谣言抑制策略进行对比分析;分别以初始传播节点、随机节点作为初始免疫节点,实施多社区免疫策略并对比谣言抑制效果。实验结果表明,多社区免疫抑制策略在谣言传播的中后期能更有效地抑制谣言的传播,在谣言传播源开始执行免疫策略更有利于消除谣言造成的危害。
The policies for inhibiting rumors behaved insufficiently in mid-and-late stage of rumors propagation on online social network. A new multi-community immune policy was proposed to inhibit rumors propagation in this period. This policy firstly spotted one or several communities of special immune node on social network, and immunized the most important neighbor node in each community with a certain probability. Using Facebook friendship as simulating experiment data, several rumors inhibition policies, including multi-community immunity, were evaluated and analyzed comparatively. With initial propagation node and random-selected nodes applied separately, comparative experiment was conducted to estimate the effect of multi-community immunity policy. Experiment result indicates that multi-community immune policy has a better inhibiting effect in mid-and-late stage of rumors propagation. Using initial propagation nodes as initial immune nodes, multi-community immune policy can eliminate the harm caused by rumors efficiently.
The policies for inhibiting rumors behaved insufficiently in mid-and-late stage of rumors propagation on online social network. A new multi-community immune policy was proposed to inhibit rumors propagation in this period. This policy firstly spotted one or several communities of special immune node on social network, and immunized the most important neighbor node in each community with a certain probability. Using Facebook friendship as simulating experiment data, several rumors inhibition policies, including multi-community immunity, were evaluated and analyzed comparatively. With initial propagation node and random-selected nodes applied separately, comparative experiment was conducted to estimate the effect of multi-community immunity policy. Experiment result indicates that multi-community immune policy has a better inhibiting effect in mid-and-late stage of rumors propagation. Using initial propagation nodes as initial immune nodes, multi-community immune policy can eliminate the harm caused by rumors efficiently.
引文
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[2] 黄宏程,蒋艾玲,胡敏.基于社交网络的信息传播模型分析[J].计算机应用研究,2016,33(9):2738-2742.
[3] 谭娟.基于传染病模型的社交网络舆情话题传播[J].计算机工程与应用, 2015,51(12):118-122.
[4] 王辉,韩江洪,邓林,等.基于移动社交网络的谣言传播动力学研究[J].物理学报,2013,62(11):110505.
[5] 万佑红,王小初.考虑从众效应的谣言传播模型[J].计算机应用,2016,36(9):2381-2385.
[6] R Pastor-Satorras, A Vespignani. Immunization of complex networks [J]. Physical Review E, 2002,65(3):036104.
[7] R Cohen, S Havlin, D Ben-Avraham. Efficient immunization strategies for computer networks and populations[J]. Physical Review Letters, 2003,91(24):247901.
[8] D Xiao, S Ruan. Global analysis of an epidemic model with nonmonotone incidence rate[J]. Mathematical Biosciences, 2007,208(2):419-429.
[9] 顾亦然,夏玲玲.在线社交网络中谣言的传播与抑制[J].物理学报,2012,61(23):238701.
[10] 李涵曼,张志勇,赵长伟.基于SIR模型的社交网络推手节点发现及信息传播抑制[J].计算机应用与软件,2016,33(6):118-121.
[11] 王英,孙福明,姜笑君.基于综合免疫控制策略的社交网络谣言传播建模[J].辽宁工业大学学报(自然科学版), 2014,34(2):71-74.
[12] 龚尚福,陈婉璐,贾澎涛.层次聚类社区发现算法的研究[J].计算机应用研究,2013,30(11):3216-3220.
[13] J Leskovec, K J Lang, M Mahoney. Empirical comparison of algorithms for network community detection[C]// Proc of the 19th International Conference on World Wide Web, 2010. New York: ACM Press, 2010:631-640.
[14] lilimu123. Facebook社交网络数据[EB/OL].北京:数据堂, 2013-01-18, [2016-12-20]. http://www.datatang.com /data/43939.
[15] 刘旭,易东云.基于局部相似性的复杂网络社区发现方法[J].自动化学报,2011,37(12):1520-1529.