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Fast determination of structurally cohesive subgroups in large networks
Robert S Sinkovits1, James Moody2,3, B Tolga Oztan4
1San Diego Supercomputer Center, University of California, San Diego, United States.
Journal of Computational Science
|May 16, 2017
Summary
This study introduces a novel computational method to efficiently identify structurally cohesive subgroups in large networks. The approach enables robust network analysis previously limited by computational intensity.
Area of Science:
- Network Science
- Graph Theory
- Computational Complexity
Background:
- Structurally cohesive subgroups are key to understanding network robustness and identifying strong connections between distant vertices.
- Identifying these subgroups is computationally intensive, limiting analyses to smaller graphs.
- Existing methods struggle with the scale of real-world networks.
Purpose of the Study:
- To develop a computationally efficient method for identifying structurally cohesive subgroups in large networks.
- To overcome the limitations of existing algorithms in analyzing large-scale graph data.
Main Methods:
- The study proposes an iterative approach leveraging cliques, k-cores, and vertex separators.
- This method reduces graph complexity, enabling the application of standard algorithms.
- The approach was tested on a large co-authorship network dataset.
Main Results:
- The novel method successfully analyzed a 29,462-vertex biconnected component from a 128,151-vertex co-authorship network.
- This demonstrates the scalability and effectiveness of the proposed approach.
- The method allows for empirical assessments of cohesion on previously intractable graph sizes.
Conclusions:
- The developed method significantly enhances the ability to analyze network cohesion in large-scale datasets.
- This facilitates a deeper understanding of network robustness and structure.
- The approach opens new avenues for empirical research in network science.
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