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Characterizing dissimilarity of weighted networks.
Yuanxiang Jiang1, Meng Li1, Ying Fan1
1School of Systems Science, Beijing Normal University, Beijing, 100875, China.
Scientific Reports
|March 12, 2021
Summary
We developed a weighted network dissimilarity metric (WD-metric) to quantify differences in weighted networks. This new metric effectively captures weight influences on network structure and aids in complex network analysis.
Area of Science:
- Complex networks
- Network science
- Data analysis
Background:
- Measuring network dissimilarities is crucial across many scientific fields.
- Existing methods like the D-measure are limited to unweighted networks.
Purpose of the Study:
- To propose a quantitative dissimilarity metric for weighted networks.
- To extend the D-measure for analyzing weighted network structures.
Main Methods:
- Developed the weighted network dissimilarity metric (WD-metric).
- Constructed a distance probability matrix for weighted networks.
- Defined complementary graphs and alpha centrality for weighted networks.
Main Results:
- The WD-metric effectively captures the influence of weights on network structure.
- Demonstrated quantitative measurement of dissimilarity between weighted networks.
- Validated the WD-metric using synthetic and real-world network data.
Conclusions:
- The WD-metric provides a robust measure for weighted network comparison.
- It can serve as a criterion for backbone extraction in complex networks.
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