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Distances between graphs typically build on the Hamming distance, which is a very general measure and forms a metric on the space of graphs, be they directed or undirected.
Most graph randomisation protocols, including the one used in this paper, are defined via a degree preserving MCMC dynamics in the space of graphs, which is defined such that it produces a relaxation towards an equilibrium state where all acceptable graphs are generated with prescribed probabilities.
A random graph model is a probability space of graphs.
The set of graphs G represents the sample space of graphs in the model, and, { x i }, i = 1 … r, represent a set of r empirical observations.
Alternatively, MCMC could be used over the discrete space of reaction graphs (Ellis and Wong, 2008) or the joint space of graphs and parameters (Oates et al., 2012).
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Greedy Equivalent Search GESS) is an effective algorithm for Bayesian network structure learning problem, which searches in the space of graph equivalence classes.
In order to explore the large search space of graph topologies, these reproduction operators are designed to be unbiased so that all nodes in a graph have equal chances of being selected to be swapped or mutated.
We present a theoretical framework defining the space of graph properties, a variety of mining queries enabled by the framework, techniques to handle the enormous size of the query space, and an experimental system called "F-Miner" that demonstrates the utility and feasibility of property mining.
Stable homology of spaces of graphs.
Configuration models are uniform distributions over specific spaces of graphs.
Configuration models (uniform distributions over specific spaces of graphs), Preferential Attachment models, power law degree sequences, stochastic block models, ERGMs.
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