主  題:The mean weighted first-passage time of the weighted networks with the random walk

內(nèi)容簡介:Based on the weighted networks and the self-similarity of fractals, we present some kinds of weighted networks. To explore the effect of the underlying structures, the number of copies, node weights and edge weights on the trapping efficiency, we study the trapping problem in these networks with the underlying geometries, focusing on a particular case with a perfect trap fixed at a given position. A basic quantity relevant to the trapping problem is the weighted trapping time(WTT), commonly called the mean weighted first-passage time(MWFPT). The average weighted trapping time(AWTT) is the average of weighted trapping time over all starting nodes other than the trap node. We use a recursive division method to divide these networks in order to calculate the AWTT scaling. We derive exactly the AWTT formulas and scalings of these networks. We show that the AWTT scaling exhibits a sublinear or linear dependence on network size. Finally, in weighted networks, adjusting the weight factor can profoundly affect the efficiency of random walk on the network.

報(bào)告人:戴美鳳    教授    

時(shí)  間:2018-06-13    10:00

地  點(diǎn):競慧東樓302

舉辦單位:統(tǒng)計(jì)與數(shù)學(xué)學(xué)院  澄園書院