A position paper summarizing generative models, dynamic community detection, and community-aware immunization strategies, with the takeaway that exploiting community structure improves performance.
Network reconstruction and community detection from dynamics
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abstract
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.
fields
physics.soc-ph 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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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.