Identifying network structure similarity using spectral graph theory.

Area of Science:

• Network Science
• Data Analysis
• Graph Theory

Background:

• Real-world networks are often too large for real-time analysis.
• Decisions are frequently based on incomplete network data.
• Assessing the similarity between inferred and true networks is crucial.

Purpose of the Study:

• To develop a novel metric for testing network similarity.
• To evaluate the performance of network inference methods.
• To provide a quantitative measure for comparing partial network information to ground truth.

Main Methods:

• Utilized a network visualization tool to generate sequential network snapshots.
• Introduced and tested a new similarity metric on these snapshots.
• Employed random matrix theory for scalability analysis on Erdös-Rényi graphs.

Main Results:

• The developed metric effectively assesses similarity between inferred and ground truth networks.
• Scalability analysis using random matrix theory provides insights into discovery process performance.
• The metric is validated on consecutive network snapshots and against ground truth.

Conclusions:

• The proposed metric offers a reliable way to evaluate network inference quality.
• This work contributes to understanding network discovery limitations and performance.
• The findings are applicable to real-time analysis of large-scale network data.