Nodes-set Mining of Express Logistics Network Based on the Key Player Problem-positive Model
WU Qitao1, LI Yuanting1,2, WU Hailing1,3, YANG Yunhao1,2, WU Junqiang4
1. Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China; 2. School of Geosciences, South China Normal University, Guangzhou 510631, China; 3. Guangdong University of Technology, Guangzhou 510090, China; 4. Nationalchip(Guangzhou), Inc, Guangzhou 510700, China
Abstract：Aiming at the problem of nodes-set mining in express logistics network, this paper constructs DW-KPP-Pos (Directed Weighted-Key Players Problem-Positive) model based on KPP-Pos (Key Player Problem-Positive) and designs a heuristic algorithm to improve the efficiency of the model. The empirical analysis of China’s urban express logistics network shows that: The DW-KPP-Pos model with heuristic algorithm can efficiently mine “Maximum spread seeds group” in express logistics network. Including Shanghai, Chongqing, Guangzhou, Beijing, Jinhua and Hong Kong; The comparison of measurement results suggest that the propagation efficiency of nodes-set K mined by DW-KPP-Pos model is 0.59%, 0.88% and 6.19% higher than that of degree nodes-set Kdeg, PageRank nodes-set Kpag and betweenness centrality nodes-set Kbet respectively. In this paper, a new method of nodes-set mining considering maximum spread effect is proposed, which can provide technical support for the layout of express logistics infrastructure.
吴旗韬, 李苑庭, 吴海玲, 杨昀昊, 武俊强. 基于关键节点积极效应模型的快递物流网络点集挖掘[J]. 复杂系统与复杂性科学, 2024, 21(4): 28-33.
WU Qitao, LI Yuanting, WU Hailing, YANG Yunhao, WU Junqiang. Nodes-set Mining of Express Logistics Network Based on the Key Player Problem-positive Model[J]. Complex Systems and Complexity Science, 2024, 21(4): 28-33.
[1] DABAGHI-ZARANDI F, KAMALIPOUR P. Community detection in complex network based on an improved random algorithm using local and global network information[DB/OL].[2023-05-06]. http://dx.chinadoi.cn/10.1016/j.jnca.2022.103492 [2] 冯芬玲,蔡明旭,贾俊杰.基于多层复杂网络的中欧班列运输网络关键节点识别研究[J].交通运输系统工程与信息,2022, 22(6):191-200. FENG F L, CAI M X, JIA J J. Research on key node identification of China railway express transportation network based on multi-layer complex network[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6):191-200. [3] 王灵丽,黄敏,高亮.基于聚类算法的交通网络节点重要性评价方法研究[J].交通信息与安全, 2020,38(2):80-88. WANG L L, WANG M, GAO L. Methods of importance evaluation of traffic network node based on clustering algorithms[J]. Journal of Transport Information and Safety,2020, 38(2): 80-88. [4] 王亭,张永,周明妮,等.融合网络拓扑结构特征与客流量的城市轨道交通关键节点识别研究[J].交通运输系统工程与信息, 2022,22(6):201-211. WANG T, ZHANG Y, ZHOU M N, et al. Identification of key nodes of urban rail transit integrating network topology characteristics and passenger flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2022,22(6):201-211. [5] 李苑君,吴旗韬,李苑庭,等.“流空间”视角下中国电子商务快递物流网络结构与机理[J].热带地理, 2023,43(4):657-668. LI Y J, WU Q T, LI Y T, et al. Exploring the structure and mechanism of China′s E-commerce express logistics network: based on space of flows[J]. Tropical Geography,2023,43(4):657-668. [6] 赵之滢,于海,朱志良,等.基于网络社团结构的节点传播影响力分析[J].计算机学报, 2014,37(4):753-766. ZHAO Z Y, YU H, ZHU Z L, et al. Identifying influential spreaders based on network community structure[J]. Chinese Journal of Computers,2014,37(4):753-766. [7] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics,2010,6(11):888-893. [8] MATTEO C, GIOVANNA F, ANTONIO I. Resilience of core-periphery networks in the case of rich-club[DB/OL].[2023-02-02]. https://doi.org/10.48550/arXiv.1708.07329. [9] BORGATTI S P. Identifying sets of key players in a social network[J]. Computational & Mathematical Organization Theory,2006,12(1):21-34. [10] 王新栋,于华,江成.社交网络关键节点检测的积极效应问题[J].中国科学院大学学报, 2019,36(3):425-432. WANG X D, YU H, JIANG C. Positive effect of key player detection in social networks[J]. Journal of University of Chinese Academy of Sciences,2019,36(3):425-432. [11] JAIN A, YADAV S, VIJ S, et al. A Novel self-organizing approach to automatic traffic light management system for road traffic network[J]. Wireless Personal Communications,2020, 110(2):1303-1321. [12] JAIN A, MITTAL K, TAYAL D K. Automatically incorporating context meaning for query expansion using graph connectivity measures[J]. Progress in Artificial Intelligence,2014, 2(2/3):129-139. [13] CHO Y, KIM W. Technology-industry networks in technology commercialization: evidence from Korean university patents[J]. Scientometrics,2014, 98(3):1785-1810. [14] MCGUIRER M, DECKRO R F. The weighted key player problem for social network analysis[J]. Military Operations Research,2015,20(2): 35-53.
