Fractality in complex networks

Critical and supercritical skeletons

J. S. Kim, Kwang-Il Goh, G. Salvi, E. Oh, B. Kahng, D. Kim

Research output: Contribution to journalArticle

84 Citations (Scopus)

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 languageEnglish
Article number016110
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume75
Issue number1
DOIs
Publication statusPublished - 2007 Feb 6

Fingerprint

Skeleton
musculoskeletal system
Complex Networks
boxes
Branching
Exponent
Fractal
Lateral
Power Law
Scaffold
Perimeter
exponents
Betweenness
fractals
Protein Interaction Networks
Centrality
Scale-free Networks
p.m.
Tile
Spanning tree

ASJC Scopus subject areas

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

Cite this

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

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 75, No. 1, 016110, 06.02.2007.

Research output: Contribution to journalArticle

@article{ffd37576faca474080680e33bc113b5b,
title = "Fractality in complex networks: Critical and supercritical skeletons",
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.",
author = "Kim, {J. S.} and Kwang-Il Goh and G. Salvi and E. Oh and B. Kahng and D. Kim",
year = "2007",
month = "2",
day = "6",
doi = "10.1103/PhysRevE.75.016110",
language = "English",
volume = "75",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "1",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=33846661167&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33846661167&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.75.016110

DO - 10.1103/PhysRevE.75.016110

M3 - Article

VL - 75

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 1

M1 - 016110

ER -