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Effects of reciprocity on random walks in weighted networks

机译:互惠对加权网络中随机游动的影响

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摘要

It has been recently reported that the reciprocity of real-life weighted networks is very pronounced, however its impact on dynamical processes is poorly understood. In this paper, we study random walks in a scale-free directed weighted network with a trap at the central hub node, where the weight of each directed edge is dominated by a parameter controlling the extent of network reciprocity. We derive two expressions for the mean first passage time (MFPT) to the trap, by using two different techniques, the results of which agree well with each other. We also analytically determine all the eigenvalues as well as their multiplicities for the fundamental matrix of the dynamical process, and show that the largest eigenvalue has an identical dominant scaling as that of the MFPT.We find that the weight parameter has a substantial effect on the MFPT, which behaves as a power-law function of the system size with the power exponent dependent on the parameter, signaling the crucial role of reciprocity in random walks occurring in weighted networks.
机译:最近有报道说,现实生活中加权网络的互易性非常明显,但是对动态过程的影响知之甚少。在本文中,我们研究了在中心集线器节点处带有陷阱的无标度有向加权网络中的随机游动,其中每个有向边的权重由控制网络互易程度的参数决定。通过使用两种不同的技术,我们得出了到陷阱的平均首次通过时间(MFPT)的两个表达式,其结果彼此吻合。我们还通过分析确定了动力学过程基本矩阵的所有特征值及其多重性,并表明最大特征值具有与MFPT相同的主导比例。 MFPT表现为系统大小的幂律函数,并且幂指数取决于该参数,表明互易性在加权网络中发生的随机游走中至关重要。

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