ArticleJournal of physics A: Mathematical and general2009
Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure.
Article in Journal of physics A: Mathematical and general, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Maximal modularity and the optimal size of parliaments.Scientific reports · 2021Article
- Estimating degree-degree correlation and network cores from the connectivity of high-degree nodes in complex networks.Scientific reports · 2020Article
- Quantifying randomness in real networks.Nature communications · 2015Article
- Bridging topological and functional information in protein interaction networks by short loops profiling.Scientific reports · 2015Article
- Statistical assessment of crosstalk enrichment between gene groups in biological networks.PloS one · 2013Article
- What you see is not what you get: how sampling affects macroscopic features of biological networks.Interface focus · 2011Article
- Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.PloS one · 2010Article
- Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure.Journal of physics A: Mathematical and general · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We study the tailoring of structured random graph ensembles to real networks, with the objective of generating precise and practical mathematical tools for quantifying and comparing network topologies macroscopically, beyond the level of degree statistics. Our family of ensembles can produce graphs with any prescribed degree distribution and any degree-degree correlation function, its control parameters can be calculated fully analytically, and as a result we can calculate (asymptotically) formulae for entropies and complexities, and for information-theoretic distances between networks, expressed directly and explicitly in terms of their measured degree distribution and degree correlations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.