Abstract
Connectivity correlations play an important role in the structure of scale-free networks. While several empirical studies exist, there is no general theoretical analysis that can explain the largely varying behavior of real networks. Here, we use scaling theory to quantify the degree of correlations in the particular case of networks with a power-law degree distribution. These networks are classified in terms of their correlation properties, revealing additional information on their structure. For instance, the studied social networks and the Internet at the router level are clustered around the line of random networks, implying a strongly connected core of hubs. On the contrary, some biological networks and the WWW exhibit strong anticorrelations. The present approach can be used to study robustness or diffusion, where we find that anticorrelations tend to accelerate the diffusion process.
| Original language | English (US) |
|---|---|
| Article number | 248701 |
| Journal | Physical Review Letters |
| Volume | 100 |
| Issue number | 24 |
| DOIs | |
| State | Published - Jun 19 2008 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Physics and Astronomy(all)
Cite this
Scaling of degree correlations and its influence on diffusion in scale-free networks. / Gallos, Lazaros K.; Song, Chaoming; Makse, Hernán A.
In: Physical Review Letters, Vol. 100, No. 24, 248701, 19.06.2008.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Scaling of degree correlations and its influence on diffusion in scale-free networks
AU - Gallos, Lazaros K.
AU - Song, Chaoming
AU - Makse, Hernán A.
PY - 2008/6/19
Y1 - 2008/6/19
N2 - Connectivity correlations play an important role in the structure of scale-free networks. While several empirical studies exist, there is no general theoretical analysis that can explain the largely varying behavior of real networks. Here, we use scaling theory to quantify the degree of correlations in the particular case of networks with a power-law degree distribution. These networks are classified in terms of their correlation properties, revealing additional information on their structure. For instance, the studied social networks and the Internet at the router level are clustered around the line of random networks, implying a strongly connected core of hubs. On the contrary, some biological networks and the WWW exhibit strong anticorrelations. The present approach can be used to study robustness or diffusion, where we find that anticorrelations tend to accelerate the diffusion process.
AB - Connectivity correlations play an important role in the structure of scale-free networks. While several empirical studies exist, there is no general theoretical analysis that can explain the largely varying behavior of real networks. Here, we use scaling theory to quantify the degree of correlations in the particular case of networks with a power-law degree distribution. These networks are classified in terms of their correlation properties, revealing additional information on their structure. For instance, the studied social networks and the Internet at the router level are clustered around the line of random networks, implying a strongly connected core of hubs. On the contrary, some biological networks and the WWW exhibit strong anticorrelations. The present approach can be used to study robustness or diffusion, where we find that anticorrelations tend to accelerate the diffusion process.
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U2 - 10.1103/PhysRevLett.100.248701
DO - 10.1103/PhysRevLett.100.248701
M3 - Article
VL - 100
JO - Physical Review Letters
JF - Physical Review Letters
SN - 0031-9007
IS - 24
M1 - 248701
ER -