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Network reconstruction and community detection from dynamics
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We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.
Forward citations
Cited by 2 Pith papers
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Bayesian inference of network structure from information cascades
A Bayesian MCMC sampler over graphs recovers network structure and edge probabilities from observed information cascades, outperforming a greedy baseline when data is scarce.
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On community structure in complex networks: challenges and opportunities
A position paper summarizing generative models, dynamic community detection, and community-aware immunization strategies, with the takeaway that exploiting community structure improves performance.
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