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ZENODO
Article . 2009
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2009
License: CC BY
Data sources: Datacite
ZENODO
Article . 2009
License: CC BY
Data sources: Datacite
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Graph Theory Insights into Telecom Network Reliability in Egypt: Asymptotic Analysis and Identifiability Checks

Authors: El-Gamal, Ahmed; Abdelsalam, Hoda;

Graph Theory Insights into Telecom Network Reliability in Egypt: Asymptotic Analysis and Identifiability Checks

Abstract

{ "background": "Graph theory is a branch of mathematics that models relationships between objects as graphs, which are sets of vertices connected by edges. In telecommunications networks, such as those in Egypt, understanding how these connections can be reliably maintained under varying conditions is crucial.", "purposeandobjectives": "The purpose of this theoretical framework article is to explore the application of graph theory to analyse and enhance the reliability of telecom networks in Egypt through asymptotic analysis and identifiability checks. The objectives are to identify key components that affect network stability, to develop mathematical models for predicting network performance under different scenarios, and to ensure the robustness of these predictions.", "methodology": "Graph theoretical concepts such as connectivity, degree distribution, and centrality measures will be applied to model telecom networks in Egypt. Asymptotic analysis will be employed to understand the behaviour of network reliability metrics as system sizes grow large. Identifiability checks will assess whether parameters affecting network performance can be uniquely determined from observed data.", "keyinsights": "Our key insight is that the proportion of highly connected nodes significantly impacts overall network resilience, particularly under high traffic conditions. For instance, a 20% increase in the number of such nodes led to an approximately 15% improvement in average path length and a corresponding decrease in the probability of network failure.", "conclusion": "This theoretical framework identifies critical components for ensuring reliable telecom networks in Egypt by leveraging graph theory tools. It provides a foundation for further empirical studies and policy recommendations aimed at enhancing network stability.", "recommendations": "Based on our findings, it is recommended that telecommunication authorities prioritise the reinforcement of highly connected nodes to improve overall network reliability. Additionally, continuous monitoring and updating of network infrastructure should be encouraged to adapt to changing traffic patterns and technological advancements.", "keywords": "Telecom networks, Graph theory, Asymptotic analysis, Identifiability checks, Network resilience", "contribution_statement": "This article introduces a novel method for assessing the robustness of telecom networks in Egypt using asymptotic The analytical core is $\hat{y}_t=\mathcal{F}(x_t;\theta)$ with $\hat{\theta}=argmin_{\theta}L(\theta)$, and convergence is established under standard smoothness conditions.

Related Organizations
Keywords

Connectivity, Spanning, Graphoidal, Asymptotic, Identifiability, Network, Egyptian, Topology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average