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- W4317436944 abstract "Many of the real-world data sets can be portrayed as bipartite networks. Since connections between nodes of the same type are lacking, they need to be inferred. The standard way to do this is by converting the bipartite networks to their monopartite projection. However, this simple approach renders an incomplete representation of all the information in the original network. To this end, we propose a new statistical method to identify the most critical links in the bipartite network projection. Our method takes into account the heterogeneity of node connections. Moreover, it can handle situations where links of different types are present. We compare our method against the state-of-the-art and illustrate the findings with synthetic data and empirical examples of investor and political data." @default.
- W4317436944 created "2023-01-19" @default.
- W4317436944 creator A5056087541 @default.
- W4317436944 date "2023-01-19" @default.
- W4317436944 modified "2023-10-18" @default.
- W4317436944 title "Inference of monopartite networks from bipartite systems with different link types" @default.
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- W4317436944 doi "https://doi.org/10.1038/s41598-023-27744-8" @default.
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- W4317436944 hasPublicationYear "2023" @default.
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