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Published on: September 25, 2021
Generalizations of the clustering coefficient to weighted complex networks
Jari Saramäki1, Mikko Kivelä, Jukka-Pekka Onnela
1Laboratory of Computational Engineering, Helsinki University of Technology, P.O. Box 9203, FIN-02015 HUT, Finland. jsaramak@lce.hut.fi
Summary
This study explores weighted complex networks by generalizing the clustering coefficient. We compare existing methods, highlighting their strengths and weaknesses for analyzing network structures.
Area of Science:
- Network Science
- Graph Theory
- Data Analysis
Background:
- Complex networks are essential in various fields.
- Link weights are crucial for accurate network analysis.
- Existing clustering coefficient measures do not fully account for link weights.
Purpose of the Study:
- To generalize the clustering coefficient for weighted complex networks.
- To compare different weighted clustering coefficient measures.
- To assess the applicability of these measures using real-world data.
Main Methods:
- Literature review of weighted clustering coefficient generalizations.
- Comparative analysis of proposed methods.
- Application to world trade and coauthorship networks.
Main Results:
- Identified advantages and limitations of various weighted clustering coefficient measures.
- Demonstrated the impact of link weights on network clustering.
- Empirical validation using real-world network datasets.
Conclusions:
- Generalizing the clustering coefficient is vital for weighted network analysis.
- The choice of measure depends on the specific network and research question.
- Further research is needed to refine and develop new weighted network metrics.
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