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| Study of “Human Flesh Search” Efficiency Combining Complex Networks with Agent-based Model |
| Wu Jiang1,2, He Chaocheng1,2, Zhu Hou3 |
1. Center for the Study of Information Resources, Wuhan University, Wuhan 430072;
2. Center of Chinese e-Commerce Research and Development, Wuhan University, Wuhan 430072;
3. School of Information Management, Sun Yat-sen University, Guangzhou 510006 |
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Abstract This study explores the influence of network topology and its nature on the efficiency of human flesh search. Based on the multi-agent modeling and simulation method, the human flesh search and simulation system is constructed from the micro-level of offline users’ search and online communication. The time cost is considered as the evaluation index of efficiency, and the system is realized based on Python NetworkX. When the average degree is small, the human flesh search efficiency of a free-scale network has obvious advantages. The core nodes of the free-scale network can effectively lower the efficiency. When the reconnection probability of a small-world network equals 0.1, the efficiency is the highest. When the average degree is large, the small-world network shows an advantage. Monotonically increasing or decreasing the average degree shows little influence on efficiency. The influence of the reconnection probability of the small-world network and controlling the core nodes of the free-scale network is not obvious. Based on multi-agent simulation, the human flesh search simulation system simulates the process of netizens’ offline and online search from a micro level. It helps the government and the network platform to use and control the human flesh search phenomenon and achieve a harmonious development of virtual network space.
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Received: 14 April 2017
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[1] Burton R M, Obel B.Computational modeling for what-is, what-might-be, and what-should-be studies—and triangulation[J]. Organization Science, 2011, 22(5): 1195-1202.
[2] 王飞跃, 李晓晨, 毛文吉, 等. 社会计算的基本方法与应用[M]. 杭州: 浙江大学出版社, 2013.
[3] Zhu H, Hu B.Agent based simulation on the process of human flesh search—From perspective of knowledge and emotion[J]. Physica A: Statistical Mechanics and its Applications, 2017, 469: 71-80.
[4] Gao D, Deng X, Zhao Q, et al.Multi-agent based simulation of organizational routines on complex networks[J]. Journal of Artificial Societies and Social Simulation, 2015, 18(3): 218-235.
[5] 朱侯. 线上-线下并行交互模式下舆论形成机制的研究[J]. 情报科学, 2016, 34(8): 19-24.
Wang F Y, Zeng D, Hendler J A, et al.A study of the human flesh search engine: Crowd-powered expansion of online knowledge[J]. Computer, 2010, 43(8): 45-53.
[6] 刘晗. 隐私权、言论自由与中国网民文化: 人肉搜索的规制困境[J]. 中外法学, 2011, 23(4): 870-879.
[7] 王程. 政策否决的社会建构——以我国几次立法禁止“人肉搜索”的失败为例[J]. 公共管理学报, 2011, 8(4): 21-31.
[8] Zhang Q, Wang F Y, Zeng D, et al.Understanding crowd-powered search groups: A social network perspective[J]. PLoS ONE, 2012, 7(6): 1-16.
[9] Nekovee M, Moreno Y, Bianconi G, et al.Theory of rumour spreading in complex social networks[J]. Physica A: Statistical Mechanics and its Applications, 2007, 374(1): 457-470.
[10] Zhou J, Liu Z, Li B.Influence of network structure on rumor propagation[J]. Physics Letters A, 2007, 368(6): 458-463.
[11] Watts D J, Strogatz S H.Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684): 440-442.
[12] 张古鹏. 小世界创新网络动态演化及其效应研究[J]. 管理科学学报, 2015, 18(6): 15-29.
[13] 吴俊, 谭跃进, 邓宏钟, 等. 无标度网络拓扑结构非均匀性研究[J]. 系统工程理论与实践, 2007, 27(5): 101-105.
[14] Davis J P, Eisenhardt K M, Bingham C B.Developing theory through simulation methods[J]. Academy of Management Review, 2007, 32(2): 480-499.
[15] Carley K M.Computational and mathematical organization theory: Perspective and directions[J]. Computational & Mathematical Organization Theory, 1995, 1(1): 39-56.
[16] 吴江. 社会网络的动态分析与仿真实验——理论与应用[M]. 武汉: 武汉大学出版社, 2012.
[17] Carley K M.Computational organizational science and organizational engineering[J]. Simulation Modelling Practice and Theory, 2002, 10(5-7): 253-269.
[18] Macal C, North M.Introductory tutorial: Agent-based modeling and simulation[C]//Proceedings of the 2014 Winter Simulation Conference. IEEE Press, 2014: 6-20.
[19] Jiang B.SimPed: Simulating pedestrian flow in a virtual urban environment[J]. Journal of Geographic Information & Decision Analysis, 1999, 3(1): 21-30.
[20] 杜先睿. 基于多智能体仿真的交通疏散问题研究[D]. 哈尔滨: 哈尔滨工业大学, 2006.
[21] Deza M M, Deza E.Encyclopedia of distances[M]. Heidelberg: Springer, 2009: 1-583.
[22] 吴娜. 泄露他人隐私行为意向的关键要素研究[D]. 大连: 大连理工大学, 2014.
[23] 王凌云, 刘锐. “人肉搜索”的传播流程及存在的问题[J]. 今传媒, 2009(2): 73-74.
[24] 吴江, 胡斌, 刘天印. 交互记忆系统影响人群与工作交互的模拟研究[J]. 管理科学, 2009, 22(1): 48-58.
[25] Hagberg A A, Schult D A, Swart P J.Exploring network structure, dynamics, and function using NetworkX[C]// Proceedings of the 7th Python in Science Conference, 2008: 11-15.
[26] 吴江, 陈君, 张劲帆. 协同创新中知识供需系统的模拟研究[J]. 现代图书情报技术, 2016, 32(9): 27-33.
[27] 谢耘耕, 荣婷. 微博传播的关键节点及其影响因素分析——基于30起重大舆情事件微博热帖的实证研究[J]. 新闻与传播研究, 2013(3): 5-15, 126.
[28] Choi J, Sang-Hyun A, Cha M S.The effects of network characteristics on performance of innovation clusters[J]. Expert Systems with Applications, 2013, 40(11): 4511-4518. |
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