A detailed characterization of complex networks using Information Theory

Area of Science:

• Network science
• Information Theory
• Complex systems analysis

Background:

• Network science relies on understanding network structure and dynamics.
• Traditional complex network metrics can be ambiguous.
• Information Theory offers robust quantitative network characterization.

Purpose of the Study:

• To apply Information Theory quantifiers for complex network analysis.
• To detect non-trivial characteristics and phase transitions in networks.
• To compare Information Theory methods with traditional network analysis approaches.

Main Methods:

• Utilizing Network Entropy as an information theory quantifier.
• Employing Network Fisher Information Measure for network analysis.
• Analyzing synthetic network models (Watts-Strogatz, Erdős-Rényi, scale-free) and real-world networks.

Main Results:

• Successfully detected transitions in Watts-Strogatz and Erdős-Rényi models.
• Identified distinct phases in scale-free networks with complex attachment mechanisms.
• Demonstrated the efficacy of Information Theory methods on real-world network data.

Conclusions:

• Network Entropy and Network Fisher Information Measure provide an efficient and robust method for network characterization.
• Information Theory offers a valuable alternative to traditional complex network analysis.
• This approach facilitates a deeper understanding of complex network dynamics and structures.