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Updated: Jul 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Random networks tossing biased coins.

F Bassetti¹, M Cosentino Lagomarsino, B Bassetti

¹Università degli Studi di Pavia, Dipartimento Matematica, Pavia, Italy. federico.bassetti@unipv.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|August 7, 2007

Summary

Researchers developed a simple algorithm to generate random directed graphs with scale-free out-degree and compact in-degree, useful for complex network analysis. This method offers analytical insights and is easily adaptable for various graph types.

Related Experiment Videos

Last Updated: Jul 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Statistical mechanics
Network science
Computational biology

Background:

Complex networks are often studied using random graph ensembles as null models.
Transcription networks present unique structural properties that require specific modeling approaches.

Purpose of the Study:

To present a simple and efficient method for generating random directed graphs.
To create ensembles with specific degree distributions: scale-free out-degree and compact in-degree.
To enable analytical tractability of network observables.

Main Methods:

Generating random directed graphs by setting adjacency matrix entries based on biased coin tosses.
Utilizing a chosen probability distribution for the biases.
Algorithm designed for efficiency and scalability.

Main Results:

The algorithm successfully generates random directed graphs with asymptotically scale-free out-degree and compact in-degree.
Effective for graphs with n approximately 100.
Many relevant network observables become analytically accessible.

Conclusions:

The proposed method provides a valuable tool for statistical mechanical studies of complex networks.
Offers improved analytical insights compared to previous methods.
The technique is versatile and generalizable to other graph structures.