Bayesian Inference of Global Statistics on Complex Networks using Random Walks
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Complex networks can be found throughout the physical sciences. This poster covers a new formalism for acquiring the global properties of complex networks via random walk sampling. This methodology only requires a very small portion of the full network to be sampled, and is general to all weighted, undirected networks. More detail and application of this topic can be found here.