Eigenvalues of transition weight matrix and eigentime identity of weighted network with two hub nodes

Zou, Jiahui; Dai, Meifeng; Wang, Xiaoqian; Tang, Hualong; He, Di; Sun, Yu; Su, Weiyi

doi:10.1139/cjp-2017-0274

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…Complex networks have been applied to many interdisciplinary fields since they show many superiority, such as communication network, airport network, biological network and so on. As a part of complex networks, deterministic networks are widely studied including the structure of network [1][2][3], spectra of the network [4][5][6][7], the applications of spectra [8][9][10] and so on.Furthermore, some dynamical processes are also hot fields in complex networks, like random walks [11][12][13], coherence problem [14,15], epidemic spreading [16], Lotka-Volterra model [17], evolutionary game [18,19], and so on.…”

Section: Introductionmentioning

confidence: 99%

Scaling of average receiving time and average shortest path on claw network

Yang¹,

Sun²,

Hao³

2020

Phys. Scr.

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A new method is used to construct the claw network. The weight of edge in next generation on claw network is related to the strength of the node in former generation. Then we calculate the exact expression of the average receiving time (ART) on claw network after dividing the whole network into four blocks. The result of ART indicates that ART grows exponentially with the network order as a power-law function. Moreover, the accurate relation of average shortest path (ASP) on claw network is determined by a novel method. The key of this method is to divide the whole claw network into four blocks, and then divide each block network into some smaller blocks to calculate ASP. The result of ASP implies that ASP increases sublinearly with the size of the claw network.

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