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Clone temporal centrality measures for incomplete sequences of graph snapshots.
1Leibniz Institute for Prevention Research and Epidemiology - BIPS, Department of Biometry and Data Management, Achterstr. 30, Bremen, Germany. hanke@leibniz-bips.de.
BMC Bioinformatics
|May 18, 2017
Summary
To improve dynamic network analysis, researchers cloned graphs in incomplete sequences to enhance temporal centrality measures. This method accurately identifies important vertices in dynamic networks, even with limited data.
Area of Science:
- Network Science
- Computational Biology
- Data Science
Background:
- Dynamic networks, representing phenomena like disease spread or gene interactions, are often analyzed using sequences of graph snapshots.
- Temporal centrality measures (e.g., betweenness, closeness) quantify vertex importance in dynamic networks but assume complete data.
- Incompletely observed graph sequences lead to biased centrality values due to missing edge information.
Purpose of the Study:
- To develop a method for accurately calculating temporal centrality measures in dynamic networks with incomplete graph sequences.
- To improve the detection of important vertices in dynamic networks despite data limitations.
Main Methods:
- Introduced graph cloning to extend existing temporal centrality metrics for incomplete graph sequences.
- Focused on temporal betweenness centrality and temporal closeness centrality as examples.
- Developed the REN algorithm for efficient calculation of temporal centrality measures, with linear computational complexity.
Main Results:
- The proposed graph cloning approach significantly improves the accuracy of detecting important vertices compared to original methods on incomplete sequences.
- Demonstrated effectiveness across various simulated scenarios of incomplete graph sequences.
- Validated the approach using an age-related gene expression dataset from the human brain.
Conclusions:
- Cloning temporal centrality measures is recommended for dynamic network analysis with incomplete graph sequences.
- The method enhances the detection rate of important vertices compared to non-compensating approaches.
- The REN algorithm efficiently calculates these enhanced centrality measures, suitable for long snapshot sequences.
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