Abstract：The measurement method of node influence based on network structure features focuses on considering the information of neighbor nodes, ignoring the difference in topology structure between nodes. To solve this problem, this paper proposes an algorithm for measuring node influence based on network structure. First, the effective distance hidden behind the network is introduced, and the importance of nodes is measured from two perspectives. Secondly, in order to overcome the influence of subjective factors in angle fusion, the influence of nodes in the complex network is calculated by using the VIKOR method in the multi-attribute decision making theory, and is sorted. In six real networks, numerical simulation experiments are carried out by using SIR model, and the ranking differentiation and accuracy are compared with other centrality algorithms. Experimental results show that the proposed method can not only obtain more accurate sorting results, but also effectively reduce the occurrence frequency of the same sorting nodes.
马媛媛, 韩华. 基于有效距离的复杂网络节点影响力度量方法[J]. 复杂系统与复杂性科学, 2022, 19(1): 12-19.
MA Yuanyuan, HAN Hua. An Effective Distance-based Measure for Node's Influence in Complex Network. Complex Systems and Complexity Science, 2022, 19(1): 12-19.
[1] BARABASI A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512. [2] JUN T, PIERA M A, Ruiz S. A causal model to explore the ACAS induced collisions[J]. Aircraft Engineering and Aerpospace Technology, 2014, 228(10): 1735-1748. [3] 汪小帆, 李翔, 陈关荣, 等. 复杂网络理论及其应用[M]. 北京: 清华大学出版社, 2006. [4] LI Y S, MA D Z, ZHANG H G, et al. Critical nodes identification of power systems based on controllability of complex networks[J]. Applied Surface Science, 2015, 5(3): 622-636. [5] MILO R, SHENORR S, ITZKOVITZ S, et al. Network motifs: simple building blocks of complex networks[J]. Science, 2002, 298(5594): 824-827. [6] ALBERT R, JEONG H, BARABASI A L. Diameter of the world-wide web[J]. Nature, 1999, 401: 130-131. [7] FREEMAN L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1): 35-41. [8] SABIDUSSI G. The centrality index of a graph[J]. Psychometrika, 1966, 31(4): 581-603. [9] BONACICH P, LLOYD P. Eigenvector-like measures of centrality for asymmetric relations[J]. Social Networks, 2001, 23(3): 191-20. [10] RADICCHI F, FORTUNATO S, MARKINES B, et al. Diffusion of scientific credits and the ranking of scientists[J]. Physical Review E, 2009, 80(5): 056103. [11] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex net-works[J]. Nature Physics, 2010, 6(11): 888-893. [12] 胡钢, 徐翔, 高浩, 等. 基于邻接信息熵的网络节点重要性识别算法[J]. 系统工程理论与实践, 2020, 40(3): 714-725. HU G, XU X, GAO H, et al. Node importance recognition algorithm based on adjacency information entropy in networks[J]. Systems Engineering-Theory & Practice, 2020, 40(3): 714-725. [13] 王凯莉, 邬春学, 艾均. 基于多阶邻居壳数的向量中心性度量方法[J]. 物理学报, 2019, 68(19): 196402. WANG K L, WU C X, AI J, et al. Complex network centrality method based on multi-order K-shell vector[J]. Acta Physica Sinica, 2019, 68(19): 235-245. [14] HU J, DU Y, MO H, et al. A modified weighted TOPSIS to identify influential nodes in complex networks[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 444: 73-85. [15] FEI L, MO H, DENG Y. A new method to identify influential nodes based on combining of existing centrality measures[J]. Modern Physics Letters B, 2017, 31(26): 1750243. [16] MA Q, MA J. Identifying and ranking influential spreaders in complex networks with consideration of spreading probability[J]. Physica A: Statistical Mechanics and Its Applications, 2017, 465: 312-330. [17] BIAN T, HU J t, DENG Y. Identifying influential nodes in complex networks based on AHP[J]. Physica A: Statistical Mechanics and Its Applications, 2017, 479(4): 1777-1787. [18] 郭强, 殷冉冉, 刘建国. 基于TOPSIS的时序网络节点重要性研究[J]. 电子科技大学学报, 2019, 48(2): 296-300. GUO Q, YIN R R, LIU J G. Node importance identification for temporal networks via the TOPSIS method[J]. Journal of University of Electronic Science and Technology of China, 2019, 48(2): 296-300. [19] 韩忠明, 吴杨, 谭旭升, 等. 面向结构洞的复杂网络关键节点排序[J]. 物理学报, 2015, 64(5): 058902. HAN Z M, WU Y, TAN X S, et al. Ranking key nodes in complex networks by considering structural holes[J]. Acta Physica Sinica, 2015, 64(5): 058902. [20] ZHAO J, WANG Y, DENG Y. Identifying influential nodes in complex networks from global perspective[J]. Chaos, Solitons & Fractals, 2020, 133: 109637. [21] BROCKMANN D, HELBING D. The hidden geometry of complex, network-driven contagion phenomena[J]. science, 2013, 342(6164): 1337-1342. [22] HU J, ZHANG X, YANG Y, et al. New doctors ranking system based on VIKOR method[J]. International Transactions in Operational Research, 2020, 27(2): 1236-1261. [23] NEWMAN M E J. Spread of epidemic disease on networks[J]. Physical Review E, 2002, 66(1): 016128. [24] YANG Y, WANG X, CHEN Y, et al. A novel centrality of influential nodes identification in complex networks[J]. IEEE Access, 2020, 8: 58742-58751. [25] KENDALL M G. The treatment of ties in ranking problems[J]. Biometrika, 1945, 33(3): 239-251. [26] ZACHARY W W. An information flow model for conflict and fission in small groups[J]. Journal of Anthropological Research, 1977, 33(4): 452-473. [27] LUSSEAU D, SCHEEIDER K, BOISSEAU O J, et al. The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations[J]. Behavioral Ecology and Sociobiology, 2003, 54(4): 396-405. [28] GLEISER P M, DANON L. Community structure in jazz[J]. Advances in Complex Systems, 2003, 6(4): 565-573. [29] GIRVAN M, NEWMAN M E J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences, 2002, 99(12): 7821-7826. [30] ZENG A, LIU W. Enhancing network robustness against malicious attacks[J]. Physical Review E, 2012, 85(6): 066130. [31] GUIMERA R, DANON L, DIAZ-gUILERA A, et al. Self-similar community structure in a network of human interactions[J]. Physical Review E, 2003, 68(6): 065103.
