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The unreasonable effectiveness of tree-based theory for networks with clustering
Date
2011
Abstract
We demonstrate that a tree-based theory for various dynamical processes operating on static, undirected networks yields extremely accurate results for several networks with high levels of clustering. We find that such a theory works well as long as the mean intervertex distance l is sufficiently small-that is, as long as it is close to the value of l in a random network with negligible clustering and the same degree-degree correlations. We support this hypothesis numerically using both real-world networks from various domains and several classes of synthetic clustered networks. We present analytical calculations that further support our claim that tree-based theories can be accurate for clustered networks, provided that the networks are "sufficiently small" worlds.
Supervisor
Description
peer-reviewed
Publisher
American Physical Society
Citation
Physical Review E;83, 036112
Files
ULRR Identifiers
Funding code
Funding Information
Science Foundation Ireland (SFI)