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

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

^①(重庆邮电大学通信与信息工程学院重庆 400065) ^②(重庆市通信软件工程技术研究中心重庆 400065)

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
计量
- 文章访问数: 1367
- HTML全文浏览量: 280
- PDF下载量: 47
- 被引次数: 0
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

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

HUANG Hongcheng^① LAI Licheng^① HU Min^① SUN Xinran^① TAO Yang^①②

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

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。
- 社交网络 /
- 信息传播 /
- 严格可控理论 /
- 弱关系
Abstract: In order to control the information propagation of the whole network at a lower cost, some information propagation control methods are introduced into social networks to select the best control point at a proper time. However, few work considers the weak ties between nodes to control the information propagation. Due to the characteristics of the complementation of information demand and the continuous assimilation of behavior orientation, the weak ties between nodes may be explosive in the process of information propagation, thus they can not be ignored. To solve this problem, considering the impact of strong and weak ties between nodes on information propagation, a propagation control method based on the exact controllability theory is proposed. Firstly, some strong ties between nodes, such as the node's intimacy, authority and interaction frequency are introduced to build the initial tie networks. Secondly, some potential valuable weak ties between nodes are identified and then tie networks are further updated. Finally, the exact controllability theory is used to find the driver node groups, and then the set of driver nodes are selected according to the characteristics of information propagation to control information propagation. Experimental results show that the proposed method can effectively promote or suppress the information propagation, which provides some ideas for the information propagation control in social networks.
- Social networks、Information propagation、Exact controllability theory、Weak ties /

HTML全文

参考文献(22)

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.

施引文献

资源附件(0)

访问统计