Simplifying Weighted Heterogeneous Networks by Extracting h-Structure via s-Degree

Area of Science:

• Network Science
• Data Mining
• Graph Theory

Background:

• Weighted heterogeneous networks are complex and challenging to analyze.
• Existing methods for network simplification often struggle with heterogeneity and weighted edges.

Purpose of the Study:

• To develop a method for extracting the core structure of weighted heterogeneous networks.
• To create a simplified representation, the h-structure, for efficient network analysis.

Main Methods:

• Transforming heterogeneous networks into homogeneous ones.
• Defining s-degree using standardized z-scores for base-nodes.
• Ranking s-degrees and applying the h-index to identify the core structure.
• Reducing adjacent edges to form the heterogeneous core structure (h-structure).

Main Results:

• The h-structure significantly simplifies weighted heterogeneous networks.
• The h-structure comprises less than 1% of the original nodes and edges.
• The method was validated on citation and co-purchase networks.

Conclusions:

• The proposed h-structure provides a highly effective simplification of weighted heterogeneous networks.
• This method facilitates deeper insights into complex network structures.
• The approach is applicable to real-world networks like citation and co-purchase networks.