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Extracting the transitivity backbone of bipartite networks
Abstract Real bipartite networks combine degree-constrained random mixing with structured connectivity balancing short and long range connections, effectively accounted for by geometric network models. We introduce a statistical filter that benchmarks node-level bipartite clustering against degree-preserving randomizations to classify nodes as geometric (signal) or degree constrained noise.
Erratum: Precision as a measure of predictability of missing links in real networks [Phys. Rev. E 101, 052318 (2020)]
Abstract Authors Article Text Original Article Abstract DOI:https://doi.org/10.1103/PhysRevE.106.069902 ©2022 American Physical Society Interdisciplinary PhysicsStatistical PhysicsNetworks Authors & Affiliations Guillermo García-Pérez, Roya Aliakbarisani, Abdorasoul Ghasemi, and M. Ángeles Serrano Click to Expand Article Text Click to Expand Original Article Click to Expand Issue Vol. 106, Iss.
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