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• W3190168498 abstract "Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding-or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here, we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization-which is characterized by modular structure and heterogeneous degrees-facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network's structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks." @default.
• W3190168498 created "2021-08-16" @default.
• W3190168498 creator A5005234637 @default.
• W3190168498 creator A5009266404 @default.
• W3190168498 date "2021-08-04" @default.
• W3190168498 modified "2023-09-26" @default.
• W3190168498 title "Quantifying the compressibility of complex networks" @default.
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• W3190168498 doi "https://doi.org/10.1073/pnas.2023473118" @default.
• W3190168498 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8364101" @default.
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• W3190168498 hasPublicationYear "2021" @default.
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