### Abstract

Fractal scaling-a power-law behavior of the number of boxes needed to tile a given network with respect to the lateral size of the box-is studied. We introduce a box-covering algorithm that is a modified version of the original algorithm introduced by Song [Nature (London) 433, 392 (2005)]; this algorithm enables easy implementation. Fractal networks are viewed as comprising a skeleton and shortcuts. The skeleton, embedded underneath the original network, is a special type of spanning tree based on the edge betweenness centrality; it provides a scaffold for the fractality of the network. When the skeleton is regarded as a branching tree, it exhibits a plateau in the mean branching number as a function of the distance from a root. For nonfractal networks, on the other hand, the mean branching number decays to zero without forming a plateau. Based on these observations, we construct a fractal network model by combining a random branching tree and local shortcuts. The scaffold branching tree can be either critical or supercritical, depending on the small worldness of a given network. For the network constructed from the critical (supercritical) branching tree, the average number of vertices within a given box grows with the lateral size of the box according to a power-law (an exponential) form in the cluster-growing method. The critical and supercritical skeletons are observed in protein interaction networks and the World Wide Web, respectively. The distribution of box masses, i.e., the number of vertices within each box, follows a power law Pm (M)∼ M-η. The exponent η depends on the box lateral size B. For small values of B, η is equal to the degree exponent γ of a given scale-free network, whereas η approaches the exponent τ=γ(γ-1) as B increases, which is the exponent of the cluster-size distribution of the random branching tree. Finally, we study the perimeter Hα of a given box α, i.e., the number of edges connected to different boxes from a given box α as a function of the box mass MB,α. It is obtained that the average perimeter over the boxes with box mass MB is likely to scale as H (MB) ∼ MB, irrespective of the box size B.

Original language | English |
---|---|

Article number | 016110 |

Journal | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics |

Volume | 75 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2007 Feb 6 |

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### ASJC Scopus subject areas

- Physics and Astronomy(all)
- Condensed Matter Physics
- Statistical and Nonlinear Physics
- Mathematical Physics

### Cite this

*Physical Review E - Statistical, Nonlinear, and Soft Matter Physics*,

*75*(1), [016110]. https://doi.org/10.1103/PhysRevE.75.016110

**Fractality in complex networks : Critical and supercritical skeletons.** / Kim, J. S.; Goh, Kwang-Il; Salvi, G.; Oh, E.; Kahng, B.; Kim, D.

Research output: Contribution to journal › Article

*Physical Review E - Statistical, Nonlinear, and Soft Matter Physics*, vol. 75, no. 1, 016110. https://doi.org/10.1103/PhysRevE.75.016110

}

TY - JOUR

T1 - Fractality in complex networks

T2 - Critical and supercritical skeletons

AU - Kim, J. S.

AU - Goh, Kwang-Il

AU - Salvi, G.

AU - Oh, E.

AU - Kahng, B.

AU - Kim, D.

PY - 2007/2/6

Y1 - 2007/2/6

N2 - Fractal scaling-a power-law behavior of the number of boxes needed to tile a given network with respect to the lateral size of the box-is studied. We introduce a box-covering algorithm that is a modified version of the original algorithm introduced by Song [Nature (London) 433, 392 (2005)]; this algorithm enables easy implementation. Fractal networks are viewed as comprising a skeleton and shortcuts. The skeleton, embedded underneath the original network, is a special type of spanning tree based on the edge betweenness centrality; it provides a scaffold for the fractality of the network. When the skeleton is regarded as a branching tree, it exhibits a plateau in the mean branching number as a function of the distance from a root. For nonfractal networks, on the other hand, the mean branching number decays to zero without forming a plateau. Based on these observations, we construct a fractal network model by combining a random branching tree and local shortcuts. The scaffold branching tree can be either critical or supercritical, depending on the small worldness of a given network. For the network constructed from the critical (supercritical) branching tree, the average number of vertices within a given box grows with the lateral size of the box according to a power-law (an exponential) form in the cluster-growing method. The critical and supercritical skeletons are observed in protein interaction networks and the World Wide Web, respectively. The distribution of box masses, i.e., the number of vertices within each box, follows a power law Pm (M)∼ M-η. The exponent η depends on the box lateral size B. For small values of B, η is equal to the degree exponent γ of a given scale-free network, whereas η approaches the exponent τ=γ(γ-1) as B increases, which is the exponent of the cluster-size distribution of the random branching tree. Finally, we study the perimeter Hα of a given box α, i.e., the number of edges connected to different boxes from a given box α as a function of the box mass MB,α. It is obtained that the average perimeter over the boxes with box mass MB is likely to scale as H (MB) ∼ MB, irrespective of the box size B.

AB - Fractal scaling-a power-law behavior of the number of boxes needed to tile a given network with respect to the lateral size of the box-is studied. We introduce a box-covering algorithm that is a modified version of the original algorithm introduced by Song [Nature (London) 433, 392 (2005)]; this algorithm enables easy implementation. Fractal networks are viewed as comprising a skeleton and shortcuts. The skeleton, embedded underneath the original network, is a special type of spanning tree based on the edge betweenness centrality; it provides a scaffold for the fractality of the network. When the skeleton is regarded as a branching tree, it exhibits a plateau in the mean branching number as a function of the distance from a root. For nonfractal networks, on the other hand, the mean branching number decays to zero without forming a plateau. Based on these observations, we construct a fractal network model by combining a random branching tree and local shortcuts. The scaffold branching tree can be either critical or supercritical, depending on the small worldness of a given network. For the network constructed from the critical (supercritical) branching tree, the average number of vertices within a given box grows with the lateral size of the box according to a power-law (an exponential) form in the cluster-growing method. The critical and supercritical skeletons are observed in protein interaction networks and the World Wide Web, respectively. The distribution of box masses, i.e., the number of vertices within each box, follows a power law Pm (M)∼ M-η. The exponent η depends on the box lateral size B. For small values of B, η is equal to the degree exponent γ of a given scale-free network, whereas η approaches the exponent τ=γ(γ-1) as B increases, which is the exponent of the cluster-size distribution of the random branching tree. Finally, we study the perimeter Hα of a given box α, i.e., the number of edges connected to different boxes from a given box α as a function of the box mass MB,α. It is obtained that the average perimeter over the boxes with box mass MB is likely to scale as H (MB) ∼ MB, irrespective of the box size B.

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U2 - 10.1103/PhysRevE.75.016110

DO - 10.1103/PhysRevE.75.016110

M3 - Article

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VL - 75

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 1

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