Abstract
We study the impact of the distance between two hubs on network coherence defined by Laplacian eigenvalues. Network coherence is a measure of the extent of consensus in a linear system with additive noise. To obtain an exact determination of coherence based on the distance, we choose a family of tree networks with two hubs controlled by two parameters. Using the tree’s regular structure, we obtain analytical expressions of the coherences with regard to network parameters and the network size. We then demonstrate that a shorter distance and a larger difference in the degrees of the two hubs lead to a higher coherence. With the same network size and distance, the best coherence occurs in the tree with the largest difference in the hub’s degrees. Finally, we establish a correlation between network coherence and average path length and find that they behave linearly.
摘要
本文研究了两个中心节点之间的距离对网络一致性的影响。网络一致性由拉普拉斯特征值所量化, 可用来衡量线性系统对外部噪声的一致性程度。为获得网络一致性关于距离的精确表达式, 选取一类由网络参数控制的具有两个中心节点的树状网络。利用其规则的拓扑结构, 得到一致性关于网络参数和网络规模的解析表达式。证明两个中心节点距离越短, 度的差异性越大, 网络一致性越好。在相同网络规模和距离下, 最大的中心节点度差异会导致最优的一致性。最后, 建立了网络一致性与平均路径长度之间的联系, 发现它们呈线性关系。
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Andrade JS, Herrmann HJ, Andrade RFS, et al., 2005. Apollonian networks: simultaneously scale-free, small world, euclidean, space filling, and with matching graphs. Phys Rev Lett, 94(1):018702. https://doi.org/10.1103/PhysRevLett.94.018702
Bamieh B, Jovanovic MR, Mitra P, et al., 2012. Coherence in large-scale networks: dimension-dependent limitations of local feedback. IEEE Trans Autom Contr, 57(9):2235–2249. https://doi.org/10.1109/TAC.2012.2202052
Barabási AL, Ravasz E, Vicsek T, 2001. Deterministic scale-free networks. Phys A Stat Mech Appl, 299(3–4):559–564. https://doi.org/10.1016/S0378-4371(01)00369-7
Chen J, Sun WG, Wang J, 2023. Topology design for leader-follower coherence in noisy asymmetric networks. Phys Scr, 98(1):015215. https://doi.org/10.1088/1402-4896/aca9a3
Comellas F, Sampels M, 2002. Deterministic small-world networks. Phys A Stat Mech Appl, 309(1–2):231–235. https://doi.org/10.1016/S0378-4371(02)00741-0
Comellas F, Ozón J, Peters JG, 2000. Deterministic small-world communication networks. Inform Process Lett, 76(1–2):83–90. https://doi.org/10.1016/S0020-0190(00)00118-6
Dai MF, Wang XQ, Zong Y, et al., 2017. First-order network coherence and eigentime identity on the weighted Cayley networks. Fractals, 25(5):1750049. https://doi.org/10.1142/S0218348X17500499
Gao HP, Zhu J, Chen X, et al., 2022. Coherence analysis of symmetric star topology networks. Front Phys, 10:876994. https://doi.org/10.3389/fphy.2022.876994
Gao L, Peng JH, Tang CM, 2021. Optimizing the firstpassage process on a class of fractal scale-free trees. Fract Fract, 5(4):184. https://doi.org/10.3390/fractalfract5040184
Gao W, Yan L, Li YF, et al., 2022a. Network performance analysis from binding number prospect. J Amb Intell Human Comput, 13:1259–1267. https://doi.org/10.1007/s12652-020-02553-3
Gao W, Chen YJ, Zhang YQ, 2022b. Viewing the network parameters and H-factors from the perspective of geometry. Int J Intell Syst, 37(10):6686–6728. https://doi.org/10.1002/int.22859
Grone R, Merris R, Sunder V, 1990. The Laplacian spectrum of a graph. SIAM J Matr Anal Appl, 11(2):218–238. https://doi.org/10.1137/0611016
Hong MD, Sun WG, Liu SY, et al., 2020. Coherence analysis and Laplacian energy of recursive trees with controlled initial states. Front Inform Technol Electron Eng, 21(6):931–938. https://doi.org/10.1631/FITEE.1900133
Hu TC, Li LL, Wu YQ, et al., 2022. Consensus dynamics in noisy trees with given parameters. Mod Phys Lett B, 36(7):2150608. https://doi.org/10.1142/S0217984921506089
Hu X, Zhang ZF, Li CD, 2021. Consensus of multi-agent systems with dynamic join characteristics under impulsive control. Front Inform Technol Electron Eng, 22(1):120–133. https://doi.org/10.1631/FITEE.2000062
Imran M, Hafi S, Gao W, et al., 2017. On topological properties of Sierpinski networks. Chaos Sol Fract, 98:199–204. https://doi.org/10.1016/j.chaos.2017.03.036
Jing T, Yang L, Sun WG, 2021. Exact calculations of network coherence in weighted ring-trees networks and recursive trees. Phys Scr, 96(8):085217. https://doi.org/10.1088/1402-4896/ac0277
Karayannakis D, Aivalis CJ, 2018. Reciprocal Vieta-type formulas and some applications. J Discr Math Sci Cryptogr, 21(1):35–39. https://doi.org/10.1080/09720529.2015.1132045
Li QS, Zaman S, Sun WT, et al., 2020. Study on the normalized Laplacian of a penta-graphene with applications. Int J Quant Chem, 120(9):e26154. https://doi.org/10.1002/qua.26154
Liu JB, Bao Y, Zheng WT, et al., 2021. Network coherence analysis on a family of nested weighted n-polygon networks. Fractals, 29(8):2150260. https://doi.org/10.1142/S0218348X21502601
Liu JB, Bao Y, Zheng WT, 2022. Analyses of some structural properties on a class of hierarchical scale-free networks. Fractals, 30(7):2250136. https://doi.org/10.1142/S0218348X22501365
Lu MB, Liu L, 2019. Leader-following consensus of second-order nonlinear multi-agent systems subject to disturbances. Front Inform Technol Electron Eng, 20(1):88–94. https://doi.org/10.1631/FITEE.1800611
Newman MEJ, 2003. The structure and function of complex networks. SIAM Rev, 45(2):167–256. https://doi.org/10.1137/S003614450342480
Olfati-Saber R, Murray RM, 2004. Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans Autom Contr, 49(9):1520–1533. https://doi.org/10.1109/TAC.2004.834113
Patterson S, Bamieh B, 2014. Consensus and coherence in fractal networks. IEEE Trans Contr Netw Syst, 1(4):338–348. https://doi.org/10.1109/TCNS.2014.2357552
Peng JH, Xiong J, Xu GA, 2014. Analysis of diffusion and trapping efficiency for random walks on non-fractal scale-free trees. Phys A Stat Mech Appl, 407:231–244. https://doi.org/10.1016/j.physa.2014.04.017
Rao S, Ghose D, 2014. Sliding mode control-based autopilots for leaderless consensus of unmanned aerial vehicles. IEEE Trans Contr Syst Technol, 22(5):1964–1972. https://doi.org/10.1109/TCST.2013.2291784
Ren W, Beard RW, Atkins EM, 2007. Information consensus in multivehicle cooperative control. IEEE Contr Syst Mag, 27(2):71–82. https://doi.org/10.1109/MCS.2007.338264
Sun WG, Ding QY, Zhang JY, et al., 2014. Coherence in a family of tree networks with an application of Laplacian spectrum. Chaos, 24(4):043112. https://doi.org/10.1063/1.4897568
Wang L, Liu ZX, 2009. Robust consensus of multi-agent systems with noise. Sci China Ser F Inform Sci, 52(5):824–834. https://doi.org/10.1007/s11432-009-0082-0
Xiao L, Boyd S, Kim SJ, 2007. Distributed average consensus with least-mean-square deviation. J Parall Distrib Comput, 67(1):33–46. https://doi.org/10.1016/j.jpdc.2006.08.010
Yi YH, Yang BJ, Zhang ZB, et al., 2022. Biharmonic distance-based performance metric for second-order noisy consensus networks. IEEE Trans Inform Theory, 68(2):1220–1236. https://doi.org/10.1109/TIT.2021.3127272
Yu WW, Chen GR, Cao M, 2010. Some necessary and sufficient conditions for second-order consensus in multiagent dynamical systems. Automatica, 46(6):1089–1095. https://doi.org/10.1016/j.automatica.2010.03.006
Yu XD, Zaman S, Ullah A, et al., 2023. Matrix analysis of hexagonal model and its applications in global mean-first-passage time of random walks. IEEE Access, 11:10045–10052. https://doi.org/10.1109/ACCESS.2023.3240468
Zaman S, 2022. Spectral analysis of three invariants associated to random walks on rounded networks with 2n-pentagons. Int J Comput Math, 99(3):465–485. https://doi.org/10.1080/00207160.2021.1919303
Zaman S, Ullah A, 2023. Kemeny’s constant and global mean first passage time of random walks on octagonal cell network. Math Methods Appl Sci, 46(8):9177–9186. https://doi.org/10.1002/mma.9046
Zaman S, Koam ANA, Khabyah AA, et al., 2022. The Kemeny’s constant and spanning trees of hexagonal ring network. CMC-Comput Mater Contin, 73:6347–6365. https://doi.org/10.32604/cmc.2022.031958
Zhang HF, Zhang J, Zhou CS, et al., 2010. Hub nodes inhibit the outbreak of epidemic under voluntary vaccination. New J Phys, 12(2):023015. https://doi.org/10.1088/1367-2630/12/2/023015
Zhang LZ, Li YY, Lou JG, et al., 2022. Bipartite asynchronous impulsive tracking consensus for multiagent systems. Front Inform Technol Electron Eng, 23(10):1522–1532. https://doi.org/10.1631/FITEE.2100122
Zhang ZZ, Zhou SG, Xie WL, et al., 2009. Standard random walks and trapping on the Koch network with scale-free behavior and small-world effect. Phys Rev E, 79(6):061113. https://doi.org/10.1103/PhysRevE.79.061113
Zhu J, Huang D, Jiang HJ, et al., 2021. Synchronizability of multi-layer variable coupling windmill-type networks. Mathematics, 9(21):2721. https://doi.org/10.3390/math9212721
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Weigang SUN guided the research. Daquan LI, Weigang SUN, and Hongxiang HU contributed to the conceptualization and methodology of the study. Daquan LI and Weigang SUN performed the formal analysis and drafted the paper. Weigang SUN and Hongxiang HU revised and finalized the paper.
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Daquan LI, Weigang SUN, and Hongxiang HU declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (Nos. 71932005 and 62376079), the Zhejiang Provincial Natural Science Foundation of China (No. LR22F030004), and the Fundamental Research Funds for the Provincial Universities of Zhejiang, China (No. GK219909299001-004)
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Li, D., Sun, W. & Hu, H. Impact of distance between two hubs on the network coherence of tree networks. Front Inform Technol Electron Eng 24, 1349–1356 (2023). https://doi.org/10.1631/FITEE.2200400
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DOI: https://doi.org/10.1631/FITEE.2200400