# Core of communities in bipartite networks

@article{Bongiorno2017CoreOC, title={Core of communities in bipartite networks}, author={Christian Bongiorno and Andr{\'a}s London and Salvatore Miccich{\`e} and Rosario N. Mantegna}, journal={Physical review. E}, year={2017}, volume={96 2-1}, pages={ 022321 } }

We use the information present in a bipartite network to detect cores of communities of each set of the bipartite system. Cores of communities are found by investigating statistically validated projected networks obtained using information present in the bipartite network. Cores of communities are highly informative and robust with respect to the presence of errors or missing entries in the bipartite network. We assess the statistical robustness of cores by investigating an artificial benchmark…

## 16 Citations

### Is it correct to project and detect? How weighting unipartite projections influences community detection

- Computer ScienceNetwork Science
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It is concluded that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities.

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This work applies a simple model to generate bipartite networks with prescribed community structure and test the performance of community detection using four different unipartite projection schemes, finding that a "hyperbolic" projection scheme mitigates performance issues to some extent.

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