A formalization of the generalized Pareto principle derives that exponential and normal distributions with 100 to 100,000 samples produce p values near 0.2, close to the 80/20 rule and below prior saturation conjectures.
Emergence of scaling in random networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
fields
physics.soc-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Formalization of the generalized Pareto principle and structural typicality of the 20/80-rule
A formalization of the generalized Pareto principle derives that exponential and normal distributions with 100 to 100,000 samples produce p values near 0.2, close to the 80/20 rule and below prior saturation conjectures.