融入用户合作与领导激励的社交网络知识传播模型

doi:10.11959/j.issn.1000-0801.2020284

电信科学 ›› 2020, Vol. 36 ›› Issue (10): 172-182.doi: 10.11959/j.issn.1000-0801.2020284

• 运营技术广角 • 上一篇

融入用户合作与领导激励的社交网络知识传播模型

顾秋阳¹,琚春华^2,³,吴功兴^2,³

¹ 浙江工业大学管理学院，浙江杭州 310023
² 浙江工商大学管理工程与电子商务学院，浙江杭州 310018
³ 浙江工商大学统计学院，浙江杭州310018

修回日期:2020-10-14 出版日期:2020-10-20 发布日期:2020-11-07
作者简介:顾秋阳（1995- ），男，浙江工业大学博士生，主要研究方向为智能信息处理、数据挖掘、中小企业高质量发展等|琚春华（1962- ），男，博士，浙江工商大学教授、博士生导师，主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等|吴功兴（1974- ），博士，浙江工商大学副教授，主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等
基金资助:
国家自然科学基金资助项目(71571162);浙江省哲学社会科学规划重点课题项目(20NDJC10Z);浙江省自然科学基金资助项目(LQ20G010002);浙江省自然科学基金资助项目(LY18D010006)

Knowledge communication model of social network with user cooperation and leadership encouragement

Qiuyang GU¹,Chunhua JU^2,³,Gongxing WU^2,³

¹ School of Management,Zhejiang University of Technology,Hangzhou 310023,China
² School of Management Science ＆Engineering,Zhejiang Gongshang University,Hangzhou 310018,China
³ Institute of Statistics,Zhejiang Gongshang University,Hangzhou 310018,China

Revised:2020-10-14 Online:2020-10-20 Published:2020-11-07
Supported by:
The National Natural Science Foundation of China(71571162);Zhejiang Philosophy and Social Science Planning Key Project(20NDJC10Z);The Natural Science Foundation of Zhejiang of China(LQ20G010002);The Natural Science Foundation of Zhejiang of China(LY18D010006)

摘要/Abstract

摘要：

近年，社交网络已经成为用户普遍使用的进行知识分享的媒介。对社交网络环境中的知识传播进行建模研究，以期为有关部门有效实施监管、提升用户的知识交流效率提供借鉴。以经典的SIS和SIR模型为基础，融入用户合作和领导激励等因子进行优化，并结合复制动态方程构建两种社交网络用户知识传播模型（SISL/SIRL）并进行分析，最后给出数值与模型算例。结果表明：存在领导者的社交网络知识传播速率更快。在社交网络知识传播过程中，易感染用户数量减少，免疫用户数量增加，感染用户数量小范围内上下波动。构建的知识传播模型相对于经典模型与真实社交网络具有更强的拟合性。

关键词: 用户合作, 领导激励, 社交网络, 知识传播

Abstract:

In recent years,social networks have become a common medium for users to share knowledge.The knowledge communication in the social network environment was modeled and researched,with a hope to provide reference for relevant departments to effectively implement supervision and improve users’ knowledge exchange efficiency.Based on the classic SIS and SIR models,factors such as user cooperation and leadership encouragement for optimization were integrated,and the replication of dynamic equations was combined to construct and analyze two social network user knowledge communication models (SISL /SIRL).Finally,the data and model examples were given.The results show that the speed of knowledge communication is faster in social networks with leaders.In the process of social network knowledge communication,the number of vulnerable users decreases while that of the immune users increases,and the number of infected users fluctuates within a small range.Compared with the classic model and the real social network,the knowledge communication model constructed has a stronger fit.

Key words: user cooperation, leadership encourage, social network, knowledge dissemination

中图分类号:

G206

顾秋阳,琚春华,吴功兴. 融入用户合作与领导激励的社交网络知识传播模型[J]. 电信科学, 2020, 36(10): 172-182.

Qiuyang GU,Chunhua JU,Gongxing WU. Knowledge communication model of social network with user cooperation and leadership encouragement[J]. Telecommunications Science, 2020, 36(10): 172-182.

图/表 10

图1

表1

图2

图3

图4

图5

图6

表2

图7

图8

参考文献 41

[1]	李纲, 巴志超 . 科研合作超网络下的知识扩散演化模型研究[J]. 情报学报, 2017,36(3): 274-284.
	LI G , BA Z C . Research on evolutionary dynamics of knowledge diffusion based on collaboration hypernetwork[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(3): 274-284.
[2]	张永云, 张生太 . 社交媒体知识协作网络中的明星效应和经纪人效应--来自Wikipedia社交媒体的发现[J]. 现代图书情报技术, 2015,1(4): 72-78.
	ZHANG Y Y , ZHANG S T . Star effect and broker effect in social media knowledge collaboration network:discovery from wikipedia social media[J]. Data Analysis and Knowledge Discovery, 2015,1(4): 72-78.
[3]	JOHNSON D W , JOHNSON R T . Learning together and alone;cooperation,competition,and individualization[J]. Nacta Journal, 1979,23(3): 214-223.
[4]	JOHNSON D W , JOHNSON R T . An educational psychology success story:Social interdependence theory and cooperative learning[J]. Educational researcher, 2009,38(5): 365-379.
[5]	GILLIES R M . The effects of cooperative learning on junior high school students during small group learning[J]. Learning and Instruction, 2004,14(2): 197-213.
[6]	MORONE P , TAYLOR R . Knowledge diffusion dynamics and network properties of face-to-face interactions[J]. Journal of Evolutionary Economics, 2004,14(3): 327-351.
[7]	朱宏淼, 张生太, 闫辛 . 微信群中隐性知识传播模型研究[J]. 科研管理, 2019,40(2): 106-115.
	ZHU H M , ZHANG S T , YAN X . A study of the tacit knowledge transmission model in a WeChat group[J]. Science Research Management, 2019,40(2): 106-115.
[8]	KISS I Z , BROOM M , CRAZE P G ,et al. Can epidemic models describe the diffusion of topics across disciplines?[J]. Journal of Informetrics, 2010,4(1): 74-82.
[9]	ZHU H M , ZHANG S T , LANG Y J . Simulation study on the effect of employee mobility on the spreading of tacit knowledge among industrial enterprises based on the knowledge spreading model[J]. Journal of Digital Information Management, 2015,13(6): 445-450.
[10]	ALVES J , DINIZP B A . Knowledge sharing in horizontal networks:The proposition of a framework[J]. Pensamiento＆Gestión, 2012,1(33): 39-66.
[11]	COWAN R , JONARD N , ZIMMERMANN J B . Evolving networks of inventors[M]// Innovation,industrial dynamics and structural transformation. Heidelberg:Springer, 2007: 129-148.
[12]	KERCHOVE C , KRINGS G , LAMBIOTTE R ,et al. Role of second trials in cascades of information over networks[J]. Physical Review E, 2009,79(1): 106-114.
[13]	WANG W , SHEN Q , CHEN Y . Community degree,clustering coefficient and knowledge propagation efficiency in complex networks[M]// Advances in Grey Systems Research. Heidelberg:Springer, 2010: 561-569.
[14]	武开, 张慧颖, 张亮 . 产业集群内隐性知识传播的仿真研究[J]. 情报学报, 2015,34(4): 371-379.
	WU K , ZHANG H Y , ZHANG L . Simulation analysis of tacit knowledge dissemination in industrial cluster[J]. Journal of the China Society for Scientific and Technical Information, 2015,34(4): 371-379.
[15]	WANLY H . The complex network model of knowledge diffusion[J]. Operations Research and Management Science, 2008,17(5): 150-154.
[16]	岳增慧, 许海云, 方曙 . 基于微分动力学的科研合作网络知识扩散模型及影响机制研究[J]. 情报学报, 2015,34(11): 1132-1142.
	YUE Z H , XU H Y , FANG S . Study on the model and influence mechanism of knowledge diffusion in scientiifc collaboration network based on differential dynamics[J]. Journal of the China Society for Scientific and Technical Information, 2015,34(11): 1132-1142.
[17]	CYR S , WEI CHOO C . The individual and social dynamics of knowledge sharing:an exploratory study[J]. Journal of Documentation, 2010,66(6): 824-846.
[18]	ZHOU T , LIU J G , BAI W J ,et al. Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity[J]. Physical Review E, 2006,74(5): 96-109.
[19]	YANG R , WANG B H , REN J ,et al. Epidemic spreading on heterogeneous networks with identical infectivity[J]. Physics Letters A, 2007,364(3-4): 189-193.
[20]	JUANG J , LIANG Y H . Analysis of a general SIS model with infective vectors on the complex networks[J]. Physica A:Statistical Mechanics and its Applications, 2015,43(7): 382-395.
[21]	ZHANG J , SUN J . Stability analysis of an SIS epidemic model with feedback mechanism on networks[J]. Physica A:Statistical Mechanics and its Applications, 2014,39(4): 24-32.
[22]	XIA C Y , MA J H , CHEN Z Q . SIR epidemic model with infection medium on complex networks[J]. Journal of Systems Engineering, 2010,25(6): 818-823.
[23]	XIA C Y , WANG L , SUN S ,et al. An SIR model with infection delay and propagation vector in complex networks[J]. Nonlinear Dynamics, 2012,69(3): 927-934.
[24]	QIAN Z , TANG S , ZHANG X ,et al. The independent spreaders involved SIR rumor model in complex networks[J]. Physica A:Statistical Mechanics and Its Applications, 2015,42(9): 95-102.
[25]	XIA L L , JIANG G P , SONG B ,et al. Rumor spreading model considering hesitating mechanism in complex social networks[J]. Physica A:Statistical Mechanics and its Applications, 2015,43(7): 295-303.
[26]	MANSUR A B F , YUSOF N . Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning[J]. Computers ＆ Education, 2013,6(3): 73-86.
[27]	CHEN R C , CHEN S Y , FAN J Y ,et al. Grouping partners for cooperative learning using genetic algorithm and social network analysis[J]. Procedia Engineering, 2012,2(9): 3888-3893.
[28]	ZHANG J P , JIN Z . The analysis of an epidemic model on networks[J]. Applied Mathematics ＆ Computation, 2011,217(17): 7053-7064.
[29]	CHEN C M , HONG C M , CHENG C C . Mining interactive social network for recommending appropriate learning partners in a Web-based cooperative learning environment[C]// 2008 IEEE conference on cybernetics and intelligent systems. Piscataway:IEEE Press, 2008: 642-647.
[30]	CHEN C M , CHANG C C . Mining learning social networks for cooperative learning with appropriate learning partners in a problem-based learning environment[J]. Interactive Learning Environments, 2014,22(1): 97-124.
[31]	LI J J , WU X J , WU X . Empirical analysis of learning network based on social network analysis,in:The 10th National Conference on Complex Networks[J]. Computers ＆ Education, 2014,23(1): 89-97.
[32]	左遥, 梁英, 毕晓迪 ,等. 社会化问答网站知识传播网络推断方法[J]. 计算机学报, 2018,41(1): 82-97.
	ZUO Y , LIANG Y , BI X D ,et al. An inference method of knowledge diffusion network in community question answering sites[J]. Chinese Journal of Computers, 2018,41(1): 82-97.
[33]	琚春华, 黄治移, 鲍福光 . 融入音乐子人格特质和社交网络行为分析的音乐推荐方法[J]. 电信科学, 2015,31(10): 122-130.
	JU C H , HUANG Z Y , BAO F G . A novel music recommendation method combining music sub-personality and social network behavior analysis[J]. Telecommunications Science, 2015,31(10): 122-130.
[34]	张岩, 韩复龄 . 基于自组织理论的网络社群知识传播研究[J]. 情报科学, 2018,36(7): 98-103.
	ZHANG Y , HAN F L . Research on knowledge dissemination of internet community based on selforganization theory[J]. Information Science, 2018,36(7): 98-103.
[35]	薛娟, 丁长青, 卢杨 . 复杂网络视角的网络众包社区知识传播研究——基于Dell公司Ideastorm众包社区的实证研究[J]. 情报科学, 2016,34(8): 25-28,61.
	XUE J , DING C Q , LU Y . Research on knowledge diffusion in network crowdsourcing community based on complex network——the empirical analysis of ideastorm crowdsourcing community of dell company[J]. Information Science, 2016,34(8): 25-28,61.
[36]	顾秋阳, 琚春华, 鲍福光 . 融入改进 SIR 模型的移动社交网络谣言传播用户群体动态演化仿真研究[J]. 情报科学, 2019,37(10): 67-74,80.
	GU Q Y , JU C H , BAO F G . Simulation research on dynamic evolution of rumor spreading user group on mobile social networks via integration improvement of SIR model[J]. Information Science, 2016,37(10): 67-74,80.
[37]	林芹, 郭东强 . 优化SIS模型的社交网络舆情传播研究——基于用户心理特征[J]. 情报科学, 2017,35(3): 53-56,75.
	LIN Q , GUO D Q . Research on the social network public opinion communication of optimized sis model——based on the user's psychological characteristics[J]. Information Science,Information Science, 2017,35(3): 53-56,75.
[38]	鞠晓伟, 张晓芝 . 组织间知识转移治理模型构建分析:基于传播能力与吸收能力角色[J]. 情报理论与实践, 2018,41(9): 83-89.
	JU X W , ZHANG X Z . Governance model construction of inter-organizational knowledge transfer:the role of dissemination capacity and absorptive capacity[J]. Information Studies:Theory ＆Application, 2018,41(9): 83-89.
[39]	王新华, 车珍, 于灏 ,等. 知识网络嵌入和知识集聚方式对组织创新力的影响差异性——知识共享意愿的视角[J]. 技术经济, 2018,37(9): 46-55,91.
	WANG X H , CHEN Z , YU H ,et al. Difference in influence of multidimensional embedding and agglomeration diversity on organization innovation:perspective of will of knowledge sharing[J]. Technology Economics, 2018,37(9): 46-55,91.
[40]	王瑞琴, 潘俊, 李一啸 . 基于多社交数据源的协同推荐方法研究[J]. 电信科学, 2015,31(6): 85-91.
	WANG R Q , PAN J , LI Y X . Research on collaborative recommendation method based on multiple data sources of social network[J]. Telecommunications Science, 2015,31(6): 85-91.
[41]	王忠义, 张鹤铭, 黄京 ,等. 基于社会网络分析的网络问答社区知识传播研究[J]. 数据分析与知识发现, 2018,2(11): 80-94.
	WANG Z Y , ZHANG H M , HUANG J ,et al. Studying knowledge dissemination of online q＆a community with social network analysis[J]. Data Analysis and Knowledge Discovery, 2018,2(11): 80-94.

参数名称	参数定义	基本参数数值
α	易感染节点在接触到感染节点后获得知识的概率	0.1
β	感染节点遗忘并再次成为易感染节点的概率	0.05
γ	受领导激励影响，易感染节点变为感染节点的概率	0.3
λ	社交网络知识传播率	—
λ_c	社交网络知识传播阈值	0.194
<k>	知识传播网络平均度	5

网络名称	节点数量/个	节点边界数/个	节点平均路径	聚类系数	平均度
Ideastorm网络	2 193	32 011	2.109	0.932	68.237
新浪微博网络	16 207	347 182	2.493	0.672	28.930

融入用户合作与领导激励的社交网络知识传播模型

Knowledge communication model of social network with user cooperation and leadership encouragement

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 41

相关文章 15

Metrics

推荐阅读 0

[1]	徐海勇,陶涛,黄岩,唐崔巍,张兆静,吴晶. 基于社交网络分析的流量红包客户挖掘与传播模式[J]. 电信科学, 2020, 36(8): 139-150.
[2]	张科,孙越佳,韩海. 基于协同过滤与社交网络混合算法的客户信用建模及授信方法[J]. 电信科学, 2020, 36(2): 52-60.
[3]	顾秋阳,吴宝,琚春华. 融入词汇共现的社交网络用户情感Biterm主题模型[J]. 电信科学, 2020, 36(11): 47-60.
[4]	李攀攀,谢正霞,乐光学,马柏林,陈丽,刘建华. SSDHT：基于社交网络的DHT安全增强机制[J]. 电信科学, 2018, 34(11): 10-20.
[5]	王莉,冯志勇,张平. 面向分布式缓存系统的无线资源管理：动机、挑战与方法[J]. 电信科学, 2017, 33(3): 83-94.
[6]	廖建新. 大数据技术的应用现状与展望[J]. 电信科学, 2015, 31(7): 1-12.
[7]	王瑞琴,潘俊,李一啸. 基于多社交数据源的协同推荐方法研究[J]. 电信科学, 2015, 31(6): 68-74.
[8]	沈士根,黄龙军,胡珂立,李宏杰,韩日昇,曹奇英. 基于微分博弈的在线社交网络恶意程序传播优化控制方法[J]. 电信科学, 2015, 31(10): 66-73.
[9]	亓晋,许斌,胡筱旋,徐匾珈,肖星琳. 基于用户行为认知的在线社交网络协同推荐[J]. 电信科学, 2015, 31(10): 108-114.
[10]	琚春华,黄治移,鲍福光. 融入音乐子人格特质和社交网络行为分析的音乐推荐方法[J]. 电信科学, 2015, 31(10): 115-123.
[11]	顾磊,王艺. 基于政府数据开放的智慧城市构建 ^*[J]. 电信科学, 2014, 30(11): 38-43.
[12]	孙晓晨,徐雅斌. 位置社交网络的潜在好友推荐模型研究[J]. 电信科学, 2014, 30(10): 71-77.
[13]	陈康,黄晓宇,王爱宝,陶彩霞,关迎晖,李磊. 基于位置信息的用户行为轨迹分析与应用综述[J]. 电信科学, 2013, 29(4): 118-124.
[14]	漆晨曦. 电信企业大数据分析、应用及管理发展策略[J]. 电信科学, 2013, 29(3): 12-16.
[15]	陈志竞,梁伯瀚. 数据挖掘助力精细化流量经营[J]. 电信科学, 2012, 28(7): 1-5.