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Abstract
In this paper a family of weighted fractal networks, in which the weights of edges have been assigned to different values with certain scale, are studied. For the case of the weighted fractal networks the definition of modified box dimension is introduced, and a rigorous proof for its existence is given. Then, the modified box dimension depending on the weighted factor and the number of copies is deduced. Assuming that the walker, at each step, starting from its current node, moves uniformly to any of its nearest neighbors. The weighted time for two adjacency nodes is the weight connecting the two nodes. Then the average weighted receiving time (AWRT) is a corresponding definition. The obtained remarkable result displays that in the large network, when the weight factor is larger than the number of copies, the AWRT grows as a power law function of the network order with the exponent, being the reciprocal of modified box dimension. This result shows that the efficiency of the trapping process depends on the modified box dimension: the larger the value of modified box dimension, the more efficient the trapping process is.
Highlights
Receiving time (AWRT) is the sum of mean weighted first-passage times (MFPTs) for all nodes absorpt at the trap located at a given target node[16,17,18]
In 2013, Dai et al introduced the non-homogenous weighted Koch networks depending on the three weight factors[19]
Based on weighted fractal networks[21], we introduce a family of the weighted fractal networks depending on the number of copies s and the weight factor r
Summary
Let E (Gn) be the set of edges image of the labeled node in Gn. The weighted fractal networks are set up. The method works as follows: we partition the nodes into boxes of size lB. Of the weighted fractal networks the the box original definition of box dimension is infinite. It is worth mentioning, our new concept of dimension does exist and is finite for this model as Theorem 3.3 shows. K→∞n→∞ −log lk where lk = diam(Gk) + 1 and Bkn denotes the minimal number of boxes of size lk that we need to cover Gn. Theorem 3.3. For the weighted fractal networks the modified box dimension:.
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