Homogeneous complex networks
Leszek Bogacz, Zdzislaw Burda, Bartlomiej Waclaw
TL;DR
This paper introduces a versatile Monte-Carlo method for generating homogeneous complex networks from various statistical ensembles, enabling tailored network construction and advanced motif analysis.
Contribution
It presents a general Monte-Carlo approach applicable to micro-canonical, canonical, and grand-canonical ensembles for homogeneous networks.
Findings
Effective network generation with desired properties
Applicable to multiple statistical ensembles
Facilitates advanced motif searching
Abstract
We discuss various ensembles of homogeneous complex networks and a Monte-Carlo method of generating graphs from these ensembles. The method is quite general and can be applied to simulate micro-canonical, canonical or grand-canonical ensembles for systems with various statistical weights. It can be used to construct homogeneous networks with desired properties, or to construct a non-trivial scoring function for problems of advanced motif searching.
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