New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

Figure 6

Tests of biCNEQ on synthetic networks with parameters set as Table 1. For each network, we performed 10 runs of 50 000 Monte Carlo sweeps each. (a) The fraction of correct number of communities in the approximation network of type-a vertices found by biCNEQ against classical projection as a function of λ. Each point shows the results from the run that finds the highest average likelihood. (b) The success estimation rate SER of biCNEQ tested on the approximation network of type-a of synthetic network with against as a function of λ. (c) The success estimation rate SER of biCNEQ tested on the approximation network of type-a of synthetic network with different values of λ as a function of the true number of communities .

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