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基于严格可控理论的社交网络信息传播控制方法

黄宏程 赖礼城 胡敏 孙欣然 陶洋

黄宏程, 赖礼城, 胡敏, 孙欣然, 陶洋. 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/JEIT170966
引用本文: 黄宏程, 赖礼城, 胡敏, 孙欣然, 陶洋. 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/JEIT170966
HUANG Hongcheng, LAI Licheng, HU Min, SUN Xinran, TAO Yang. 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
Citation: HUANG Hongcheng, LAI Licheng, HU Min, SUN Xinran, TAO Yang. 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

基于严格可控理论的社交网络信息传播控制方法

doi: 10.11999/JEIT170966
基金项目: 

国家自然科学基金(61371097),重庆市科委基础与前沿研究计划项目(cstc2014jcyjA40039)

详细信息
    作者简介:

    黄宏程: 男,1979年生,副教授,研究方向为复杂网络与信息传播理论、社会感知与智能计算. 赖礼城: 男,1992年生,硕士生,研究方向为信息物理社会系统融合. 胡 敏: 女,1971年生,副教授,研究方向为信息通信网络体系结构、人机交互理论与技术应用. 孙欣然: 女,1991年生,硕士生,研究方向为社会网络信息传播与控制. 陶 洋: 男,1964年生,教授,研究方向为大数据与计算智能.

  • 中图分类号: TP393

Information Propagation Control Method in Social Networks Based on Exact Controllability Theory

Funds: 

The National Natural Science Foundation of China (61371097), The Foundation and Frontier Research Project of Chongqing Municipal Science and Technology Commission (cstc2014jcyjA40039)

  • 摘要: 社交网络信息传播控制通过在合适的时机选择最佳的控制点,以较小的代价实现对大部分甚至整个网络的信息传播控制。社交网络用户间的弱关系往往具有信息需求互补、行为取向不断同化的特点,使其在信息扩散过程中有着不可忽视甚至爆发式的传播作用。针对这一问题,考虑社交网络强弱关系对信息传播的影响,该文提出一种基于严格可控理论的信息传播控制方法。首先,针对强关系对信息传播的影响,提取用户间的亲密度、权威性以及互动频率3个影响因素,构建强度关系网络。其次,考虑到信息传播中的弱关系特性,对网络中具有潜在价值的弱关系进行识别,并对强度关系网络中的连边权值加以更新。最后,利用严格可控理论找出网络中的驱动节点组,并根据信息传播的特征选取驱动节点集,对信息传播进行控制。实验结果表明,该文所提传播控制方法能对信息传播的促进或抑制进行有效控制,为社交网络信息传播控制提供新的方法和思路。
  • 胡长军, 许文文, 胡颖, 等. 在线社交网络信息传播研究综述[J]. 电子与信息学报, 2017, 39(4): 794-804.

    doi: 10.11999/ JEIT161136.
    HU Changjun, XU Wenwen, HU Ying, et al. Review of information diffusion in online social networks[J]. Journal of Electronics & Information Technology, 2017, 39(4): 794-804. doi: 10.11999/JEIT161136.
    [2] ZHANG Zufan, ZHANG Porui, LIU Dan, et al. SRSM-based adaptive relay selection for D2D communications[J]. IEEE Internet of Things Journal, 2017, (99): 1. doi: 10.1109/JIOT. 2017.2749443.
    [3] WU Dapeng, LIU Qianru, WANG Honggang, et al. Socially aware energy-efficient mobile edge collaboration for video distribution[J]. IEEE Transaction on Multimedia, 2017, 19(10): 2197-2209. doi: 10.1109/TMM.2017.2733300.
    LI Jinjie, WU Lianren, QI Jiayin, et al. Research on information dissemination in online social network based on human dynamics[J]. Journal of Electronics & Information Technology, 2017, 39(4): 785-793. doi: 10.11999/JEIT160940.
    [5] KANDHWAY K and KURI J. Optimal resource allocation over time and degree classes for maximizing information dissemination in social networks[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 3204-3217. doi: 10.1109/TNET. 2015.2512541.
    [6] ZHANG Zhiwei and WANG Zhenyu. The data-driven null models for information dissemination tree in social networks[J]. Physica A: Statistical Mechanics and Its Applications, 2017, 484: 394-411. doi: 10.1016/j.physa.2017. 05.008.
    [7] DU Zhanwei, YANG Yongjian, CAI Qing, et al. Modeling and inferring mobile phone users’ negative emotion spreading in social networks[J]. Future Generation Computer Systems, 2017, 73(3): 993-942. doi: 10.1016/j.future.2017.04.015.
    [8] LIU Yangyu, SLOTINE J J, and BARABASI A L. Controllability of complex networks[J]. Nature, 2011, 473(7346): 167-173. doi: 10.1038/nature10011.
    [9] ZAÑUDO J G T, YANG G, and ALBERT R. Structure- based control of complex networks with nonlinear dynamics [J]. Proceedings of the National Academy of Sciences, 2017, 114(28): 7234-7239. doi: 10.1073/pnas.1617387114.
    [10] CHEN Yuzhong, WANG Lezhi , WANG Wenxu, et al. Energy scaling and reduction in controlling complex networks[J]. Royal Society Open Science, 2016, 3(4): 160064. doi: 10.1098 /rsos.160064.
    [11] OLSHEVSKY A. Minimal controllability problems[J]. IEEE Transactions on Control of Network Systems, 2014, 1(3): 249-258. doi: 10.1109/TCNS.2014.2337974.
    [12] YUAN Zhengzhong, ZHAO Chen, DI Zengru, et al. Exact controllability of complex networks[J]. Nature Communications, 2013, 4: 2447. doi: 10.1038/ncomms3447.
    [13] TONG Guangmo, WU Weili, TANG Shaojie, et al. Adaptive influence maximization in dynamic social networks[J]. IEEE/ACM Transactions on Networking (TON), 2017, 25(1): 112-125. doi: 10.1109/TNET.2016.2563397.
    [14] ROZENSHTEIN P, TATTI N, and GIONIS A. Inferring the strength of social ties: A community-driven approach[C]. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax, Canada, 2017: 1017-1025. doi: 10.1145/3097983. 3098199.
    [15] PAGANI E, VALERIO L , and ROSSI G P. Weak social ties improve content delivery in behavior-aware opportunistic networks[J]. Ad Hoc Networks, 2015, 25: 314-329. doi: 10.1016/j.adhoc.2014.07.005.
    [16] ARAL S. The future of weak ties[J]. American Journal of Sociology, 2016, 121(6): 1931-1939. doi: 10.1086/686293.
    [17] WU Dapeng, YAN Junjie, WANG Honggang, et al. Social attribute aware incentive mechanism for Device-to-Device video distribution[J]. IEEE Transaction on Multimedia, 2017, 19(8): 1908-1920. doi: 10.1109/TMM.2017.2692648.
    [18] ZHAO Jichang, WU Junjie, and XU Ke. Weak ties: subtle role of information diffusion in online social networks[J]. Physical Review E Statistical Nonlinear and Soft Matter Physics, 2010, 82(1): 016105. doi: 10.1103/PhysRevE.82. 016105.
    [19] SANDSTRON G M and DUNN E W. Social interactions and well-being: The surprising power of weak ties[J]. Personality and Social Psychology Bulletin, 2014, 40(7): 910-922. doi: 10.1177/0146167214529799.
    [20] DE DOMENICO M, LIMA A, MOUGEL P, et al. The anatomy of a scientific rumor[J]. Scientific Reports, 2013, 3(10): 2980. doi: 10.1038/srep02980.
    GU Yiran and XIA Lingling. The propagation and inhibition of rumors in online social network[J]. Acta Physica Sinica, 2012, 61(23): 544-550. doi: 10.7498/aps.61.238701.
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出版历程
  • 收稿日期:  2017-10-19
  • 修回日期:  2018-03-19
  • 刊出日期:  2018-07-19

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