RETRACTED ARTICLE: Modified box dimension and average weighted receiving time on the weighted fractal networks

  • This article was retracted on 08 April 2020


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.

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  • 08 April 2020

    "This paper has been retracted."

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The Editors have retracted this Article because significant portions of the text and equations were taken from Roland Molontay’s BSc thesis without attribution.

The following parts of the paper are copied verbatim or are adapted from those appearing in the thesis: the definition of dB and preceding and subsequent sentences; Definition 3.2; Proof of Lemma 3.4; Lemma 3.5; Proof of Lemma 3.5; Lemma 3.6; Proof of Lemma 3.6; Equation 6; Lemma 3.7; Proof of Lemma 3.7; Lemma 3.8; Equation 8; Proof of Theorem 3.3.

The authors do not agree to the retraction of the Article.

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Dai, M., Sun, Y., Shao, S. et al. RETRACTED ARTICLE: Modified box dimension and average weighted receiving time on the weighted fractal networks. Sci Rep 5, 18210 (2015).

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