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Related Experiment Video

Updated: Jun 8, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Greedy connectivity of geographically embedded graphs.

Jie Sun¹, Daniel ben-Avraham

¹Department of Mathematics & Computer Science, Clarkson University, Potsdam, New York 13699-5815, USA. sunj@clarkson.edu

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

|September 28, 2010

Summary

We developed a new metric called greedy connectivity for spatial networks. This measure evaluates how well networks connect using only local information, even with unreliable links, aiding optimal network design.

Related Experiment Videos

Last Updated: Jun 8, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Area of Science:

Network Science
Graph Theory
Spatial Analysis

Background:

Geographical networks are crucial for various applications.
Designing efficient networks often involves local information and imperfect links.
Existing connectivity measures may not fully capture these real-world constraints.

Purpose of the Study:

Introduce a novel metric: greedy connectivity for geographical networks.
Quantify network performance based on local information and transmission reliability.
Provide a criterion for optimizing network design.

Main Methods:

Define greedy connectivity for spatial graphs.
Incorporate local search strategies for pathfinding.
Account for probabilistic link failures and node reachability.

Main Results:

Greedy connectivity is larger with higher node reachability probabilities.
The metric effectively evaluates networks using local information.
Demonstrate the metric's utility in network design scenarios.

Conclusions:

Greedy connectivity offers a practical measure for spatial networks.
The metric accounts for realistic communication constraints.
It serves as a valuable tool for designing robust and efficient networks.