Efficiently inferring community structure in bipartite networks

@article{Larremore2014EfficientlyIC,
  title={Efficiently inferring community structure in bipartite networks},
  author={Daniel B. Larremore and Aaron Clauset and Abigail Z. Jacobs},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2014},
  volume={90 1},
  pages={
          012805
        }
}
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by… 

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