Phase transitions in semisupervised clustering of sparse networks

@article{Zhang2014PhaseTI,
  title={Phase transitions in semisupervised clustering of sparse networks},
  author={P. Zhang and C. Moore and L. Zdeborov{\'a}},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2014},
  volume={90 5-1},
  pages={
          052802
        }
}
  • P. Zhang, C. Moore, L. Zdeborová
  • Published 2014
  • Mathematics, Medicine, Computer Science, Physics
  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology. However, if we know the correct labels of some fraction α of the nodes, we can do better. We study the phase diagram of this semisupervised learning problem for networks… CONTINUE READING
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