Information Propagation Control Method in Mobile Social Networks Based on Network Motifs
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摘要: 移动社会网络中的信息传播具有突发性、多元性、偏差性等特点，使得相关话题和事件能够在短时间内形成强大的网络和社会舆论场，这有可能被恶意用户利用来散布谣言，给网络环境带来了恶劣的影响。针对这一问题，该文提出一种基于网络模体的信息可控传播方法。首先，提出多实体的竞争性独立级联模型(MCIC)，该模型在信息竞争扩散理论的基础上，首次结合社会网络层用户的社会属性，来感知恶意信息并控制信息传播。其次，该文定义了控制信息流模体(CIFM)，并选择出具有可控传播功能的关键网络模体，设计其在通信层的高效可控传播算法。最后，通过理论推导证明了该文方法具有收敛性。仿真实验表明，与其他方法相比，所提方法不仅在信息传播中最大感染时间和平均感染时间上更有优势，而且在控制信息传播方面的效果也是最好的。Abstract: The abruptness, diversity, and deviation of public opinion information in mobile social networks may encourage malicious users to spread rumors and has a bad impact on the network environment. To solve this problem, a controllable information propagation method based on network motif is proposed. Firstly, a Multi-entity Competitive Independent Cascade (MCIC)model in the social network layer is established. Secondly, this paper defines the Control Information Flow Motif (CIFM), determine the key network motifs and designs its efficient and controllable propagation algorithm in the communication layer. Finally, Theoretical derivation proves that this method has convergence, and the simulation results show that the proposed method not only has more advantages in terms of time efficiency, but also has the best effect in controlling information propagation.
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算法1 遏制恶意信息的种子节点选取算法 输入：社会网络层中所有网络模体${M_s} $；网络$G $中不包含在模体
中的节点$ {u_i} $输出：前k个种子节点集合S (1) 初始化社会网络$G $ (2) 初始化所有模体${M_s} $ (3) 根据式(5)计算所有模体的关键程度 (4) for $ i = l $ to $i = k $ (5) 生成排序先后为${M_1},{M_2}, \cdots ,{M_k} $，并选择关键程度最高的模
体$ {M_i} $(6) 分别比较${M_i} $中${d_{{v_1}}},{d_{{v_2}}},{d_{{v_3}}} $大小，取度最大的节点作为第1
个种子节点放入S中(7) 将其余含有该种子节点的模体删除 (8) end for (9)获取前k个遏制传播恶意信息的种子节点集合S 
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算法2 信息可控传播算法 输入：${I_i} $，${N_{{\text{ic}}}} $，${V_c}(t) $ 输出：收敛的传播恶意信息节点集合$ \left| {{V_m}({t_i})} \right| $ (1) 对于${t_1} $时刻的任意一条信息${I_i} $，社会层用户${N_{{\text{ic}}}} $将感知到信息
的数据传到通信层(2)通信层的邻居节点接受正确信息和错误信息的概率分别为$p $和
${p_m} $(3) 依照SMCR传播模型计算${t_2} $时刻传播恶意信息的节点数：
${V_m}({t_2})| = |{V_m}({t_1})| + {p_m} \cdot |{V_n}({t_1})| $(4) 令$t = 1,2, \cdots ,i$，按照步骤(3)的方法计算 (5) 直到$ \left| {{V_m}({t_i})} \right| $的大小不再增加 (6) end for (7) 得到一个收敛的$\left| {{V_m}({t_i})} \right| $
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[1] WU Jie and WANG Yunsheng. Hypercube-based multipath social feature routing in human contact networks[J]. IEEE Transactions on Computers, 2014, 63(2): 383–396. doi: 10.1109/TC.2012.209 [2] LIN Limei, XU Li, HUANG Yanze, et al. On exploiting priority relation graph for reliable multi-path communication in mobile social networks[J]. Information Sciences, 2019, 477: 490–507. doi: 10.1016/j.ins.2018.10.035 [3] GAO Chuangen, DU Hai, WU Weili, et al. Viral marketing of online game by DS decomposition in social networks[J]. Theoretical Computer Science, 2020, 803: 10–21. doi: 10.1016/j.tcs.2019.03.006 [4] ETESAMI S R, BOLOUK S, NEDIĆ A, et al. Influence of conformist and manipulative behaviors on public opinion[J]. IEEE Transactions on Control of Network Systems, 2019, 6(1): 202–214. doi: 10.1109/TCNS.2018.2806179 [5] 张宪立, 唐建新, 曹来成. 基于反向PageRank的影响力最大化算法[J]. 计算机应用, 2020, 40(1): 96–102. doi: 10.11772/j.issn.1001-9081.2019061066ZHANG Xianli, TANG Jianxin, and CAO Laicheng. Influence maximization algorithm based on reverse PageRank[J]. Journal of Computer Applications, 2020, 40(1): 96–102. doi: 10.11772/j.issn.1001-9081.2019061066 [6] 顾益军, 解易, 张培晶. 面向有组织犯罪分析的人际关系网络节点重要性评价研究[J]. 中国人民公安大学学报:自然科学版, 2013, 19(4): 66–68.GU Yijun, XIE Yi, and ZHANG Peijing. Research on the importance evaluation of interpersonal network nodes for organized crime analysis[J]. Journal of People’s Public Security University of China:Science and Technology, 2013, 19(4): 66–68. [7] BÓTA A and KRÉSZ M. A high resolution clique-based overlapping community detection algorithm for small-world networks[J]. Informatica, 2015, 39(2): 177–187. [8] 黄宏程, 赖礼城, 胡敏, 等. 基于严格可控理论的社交网络信息传播控制方法[J]. 电子与信息学报, 2018, 40(7): 1707–1714. doi: 10.11999/JEIT170966HUANG Hongcheng, LAI Licheng, HU Min, et al. Information propagation control method in social networks based on exact controllability theory[J]. Journal of Electronics &Information Technology, 2018, 40(7): 1707–1714. doi: 10.11999/JEIT170966 [9] 陈慧敏, 金思辰, 林微, 等. 新冠疫情相关社交媒体谣言传播量化分析[J]. 计算机研究与发展, 2021, 58(7): 1366–1384. doi: 10.7544/issn1000-1239.2021.20200818CHEN Huimin, JIN Sichen, LIN Wei, et al. Quantitative analysis on the communication of COVID-19 related social media rumors[J]. Journal of Computer Research and Development, 2021, 58(7): 1366–1384. doi: 10.7544/issn1000-1239.2021.20200818 [10] KEMPE D, KLEINBERG J, and TARDOS É. Maximizing the spread of influence through a social network[C]. The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, USA, 2003: 137–146. [11] PENG Sancheng, WANG Guojun, ZHOU Yongmei, et al. An immunization framework for social networks through big data based influence modeling[J]. IEEE Transactions on Dependable and Secure Computing, 2019, 16(6): 984–995. doi: 10.1109/TDSC.2017.2731844 [12] DOOSTMOHAMMADIAN M, RABIEE H R, and KHAN U A. Centrality-based epidemic control in complex social networks[J]. Social Network Analysis and Mining, 2020, 10(1): 32. doi: 10.1007/s13278-020-00638-7 [13] CHANG S, PIERSON E, KOH P W, et al. Mobility network models of COVID-19 explain inequities and inform reopening[J]. Nature, 2021, 589(7840): 82–87. doi: 10.1038/s41586-020-2923-3 [14] WANG Zhishuang, XIA Chengyi, CHEN Zengqiang, et al. Epidemic propagation with positive and negative preventive information in multiplex networks[J]. IEEE Transactions on Cybernetics, 2021, 51(3): 1454–1462. doi: 10.1109/TCYB.2019.2960605 [15] HU Xiping, CHU T H S, LEUNG V C M, et al. A survey on mobile social networks: Applications, platforms, system architectures, and future research directions[J]. IEEE Communications Surveys & Tutorials, 2015, 17(3): 1557–1581. doi: 10.1109/COMST.2014.2371813 [16] 顾洁, 王筱纶, 胡安安. 社交网络信息竞争扩散的关键节点策略研究[J]. 情报科学, 2020, 38(3): 78–86. doi: 10.13833/j.issn.1007-7634.2020.03.013GU Jie, WANG Xiaolun, and HU An’an. Seeding strategy of competitive diffusion in social network[J]. Information Science, 2020, 38(3): 78–86. doi: 10.13833/j.issn.1007-7634.2020.03.013 [17] COLIZZA V, FLAMMINI A, SERRANO M A, et al. Detecting rich-club ordering in complex networks[J]. Nature Physics, 2006, 2(2): 110–115. doi: 10.1038/nphys209 [18] GHOSHAL A K, DAS N, and DAS S. Influence of community structure on misinformation containment in online social networks[J]. Knowledge-Based Systems, 2021, 213: 106693. doi: 10.1016/j.knosys.2020.106693 [19] SAXENA A, MALIK V, and IYENGAR S R S. Estimating the degree centrality ranking of a node[J]. Social Analysis and Mining, 2018, 8(1): 1–20. -
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