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Maximum-entropy Random Graph Model
Maximum-entropy random graph models are random graph models used to study complex networks subject to the principle of maximum entropy under a set of structural constraints, which may be global, distributional, or local. Overview Any random graph model (at a fixed set of parameter values) results in a probability distribution on graph (discrete mathematics), graphs, and those that are maximum entropy within the considered class of distributions have the special property of being maximally unbiased null models for network inference (e.g. biological network inference). Each model defines a family of probability distributions on the set of graphs of size n (for each n>n_0 for some finite n_0), parameterized by a collection of constraints on J observables \_^J defined for each graph G (such as fixed expected average degree (graph theory), degree, degree distribution of a particular form, or specific Degree (graph theory)#Degree sequence, degree sequence), enforced in the graph distribu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Random Graph
In mathematics, random graph is the general term to refer to probability distributions over graphs. Random graphs may be described simply by a probability distribution, or by a random process which generates them. The theory of random graphs lies at the intersection between graph theory and probability theory. From a mathematical perspective, random graphs are used to answer questions about the properties of ''typical'' graphs. Its practical applications are found in all areas in which complex networks need to be modeled – many random graph models are thus known, mirroring the diverse types of complex networks encountered in different areas. In a mathematical context, ''random graph'' refers almost exclusively to the Erdős–Rényi random graph model. In other contexts, any graph model may be referred to as a ''random graph''. Models A random graph is obtained by starting with a set of ''n'' isolated vertices and adding successive edges between them at random. The a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |