Clustering signatures classify directed networks

Area of Science:

• Network Science
• Graph Theory
• Data Analysis

Background:

• The clustering coefficient is a key metric for analyzing network structure.
• Generalizations of the clustering coefficient are needed for directed networks.
• Understanding network properties requires robust classification methods.

Purpose of the Study:

• To introduce and apply a novel clustering signature for directed networks.
• To evaluate the effectiveness of clustering signatures in classifying diverse network types.
• To demonstrate the utility of clustering signatures as a network classifier.

Main Methods:

• Analysis of 16 real-world directed networks from five categories.
• Application of a generalized clustering coefficient to generate network signatures.
• Comparative analysis of network signatures in a multi-dimensional space.

Main Results:

• Five distinct network classes (social, genetic, word, food webs, electric circuits) were clearly separated.
• The separation is attributed to the statistical properties of local network neighborhoods.
• Clustering signatures proved to be a powerful tool for network classification.

Conclusions:

• Clustering signatures offer a robust method for classifying directed networks.
• The approach highlights inherent statistical differences across various network types.
• This method advances the analysis and understanding of complex systems.