• Corpus ID: 207870684

Phase transitions and optimal algorithms in the semi-supervised classfications in graphs: from belief propagation to convolution neural networks

@article{Zhou2019PhaseTA,
  title={Phase transitions and optimal algorithms in the semi-supervised classfications in graphs: from belief propagation to convolution neural networks},
  author={Pengfei Zhou and P. Zhang},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.00197}
}
By analyzing Bayesian inference of generative model for random networks with both relations (edges) and node features (discrete labels), we perform an asymptotically exact analysis of the semi-supervised classfication problems on graph-structured data using the cavity method of statistical physics. We unveil detectability phase transitions which put fundamental limit on ability of classfications for all possible algorithms. Our theory naturally converts to a message passing algorithm which… 

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