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

HUANG Hongcheng; LAI Licheng; HU Min; SUN Xinran; TAO Yang

doi:10.11999/JEIT170966

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(7): 1707-1714

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

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

doi: 10.11999/JEIT170966

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)

Received Date: 2017-10-19
Rev Recd Date: 2018-03-19
Publish Date: 2018-07-19

Abstract

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,

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References(22)

References

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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