| 基于空间要素与随机图融合的成长型社交网络模型研究 |
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| 引用本文： | 顾秋阳,琚春华,鲍福光.基于空间要素与随机图融合的成长型社交网络模型研究[J].情报理论与实践,2020,43(3):98-104,77. |
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| 作者姓名： | 顾秋阳 琚春华 鲍福光 |
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| 作者单位： | 浙江工商大学现代商贸研究中心,浙江 杭州 310018;宁波诺丁汉大学商学院,浙江 宁波315175;浙江工商大学现代商贸研究中心,浙江 杭州 310018;浙江工商大学管理工程与电子商务学院,浙江杭州310018 |
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| 基金项目： | 浙江省自然科学基金项目“数字经济下融合线上线下行为分析的全网全息用户模型及应用研究”(项目编号:LQ20G010002);国家自然科学基金项目“电商环境下融入在线社会关系的消费信贷价值度量研究”(项目编号:71571162);浙江省软科学重点项目“基于DEA的网上技术市场运行效率评价研究”(项目编号:2018C25030)的成果之一;浙江工商大学现代商贸流通体系建设协同创新中心;教育部省部共建人文社会科学重点研究基地浙江工商大学现代商贸研究中心资助。 |
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| 摘 要： | 目的/意义]近年来社交网络已经成为人们普遍使用的分享传播信息的媒介。随着社交网络的不断膨胀,传统的社交网络模型与现实网络情况的契合度逐渐减弱。文章通过建模仿真对传统模型进行优化和检验,以期为有关部门有效应对和预测社交网络动向提供参考。方法/过程]文章以经典社交网络模型为基础,融入空间要素进行优化,并在随机图中逐渐增加节点数量以构建成长型社交网络模型,最后利用MATLAB等软件实现模型和数值仿真验算。结果/结论]实验结果显示:空间距离要素对社交网络节点间关系的变化存在较大影响,且其影响在某些情景下高于关系强度;而基于纯空间要素与纯关系要素的网络都不会使得节点产生很高的聚类反映,但当其两者以一定比例融合后,节点链接概率将大幅上升;该研究所建成长型社交网络模型相比于传统模型与真实社交网络具有更高的拟合度。
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| 关 键 词： | 空间要素 随机图 社交网络 网络重构 |
Study on Growing Social Network Model Based on Integration of Spatial Elements and Random Graphs |
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| Abstract: | Purpose/significance] In recent years,social network has become the media widely used by people for sharing and spreading information.As social network grows increasingly,the level of alignment between the traditional social network model and the actual situation became lower.This paper optimized the traditional model through model building and simulating,in the hope to provide reference to the relevant department in effectively responding to and forecasting social network trend.Method/process]This paper uses the classic social network model as the basis,integrates spatial elements for optimization,and gradually adds the number of nodes in the random graphs to establish the growing social network model,and finally uses the software such as MATLAB etc.to realize the simulation computation and checking of the model and numerical values.Result/conclusion]The experiment result indicates:space distance has a large impact on the changes among the social network nodes,and under certain circumstances the impact is higher than the relationship strength;neither the pure space network,nor the pure relationship network will incur high clustering reaction of the nodes,however,once they are both integrated in a certain proportion,the chance of node connection increases significantly;compared with the traditional models,the growing social network model built in this study better fits the actual social network. |
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| Keywords: | spatial elements random graphs social network network reconfiguration |
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