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The aim is to explore how spectral properties of adjacency matrices in large-scale social networks influence their connectivity and information flow.

June 13, 2026Open Access

Spectral Properties of Large-Scale Random Graphs: Implications for Social Network Connectivity

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The aim is to explore how spectral properties of adjacency matrices in large-scale social networks influence their connectivity and information flow.
Utilized spectral graph theory to analyze eigenvalues of adjacency matrices in various network architectures.
Examined the relationship between spectral radius and epidemic thresholds in information diffusion models.
Synthesize spectral metrics with dynamic modeling to understand the behavior of networks.
Identified extremal eigenvalues as essential indicators of information propagation thresholds.
Highlighted that spectral configurations predict network robustness against localized failures and information saturation.
Noted that degree distributions and community structures significantly influence eigenvalue behavior and information spread.

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Implication

Randomized trial investigates spectral properties of adjacency matrices impacting information diffusion and network robustness in social networks, suggesting essential pathways for optimal...

Cite This Study

Ningappа К (2013) studied this question.

synapsesocial.com/papers/6a2cf665faef96ed7f058124 https://doi.org/10.5281/zenodo.20636922

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Implication

Randomized trial investigates spectral properties of adjacency matrices impacting information diffusion and network robustness in social networks, suggesting essential pathways for optimal...

Spectral Properties of Large-Scale Random Graphs: Implications for Social Network Connectivity

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Implication

Cite This Study

Spectral Properties of Large-Scale Random Graphs: Implications for Social Network Connectivity

Key Points

Discussion

Implication

Cite This Study

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