Research Article
Community Structure Detection in Complex Networks with Applications to Gas-Liquid Two-Phase Flow
@INPROCEEDINGS{10.1007/978-3-642-02469-6_68, author={Zhongke Gao and Ningde Jin}, title={Community Structure Detection in Complex Networks with Applications to Gas-Liquid Two-Phase Flow}, proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2}, proceedings_a={COMPLEX PART 2}, year={2012}, month={5}, keywords={complex networks community structure detection data field theory two-phase flow pattern identification}, doi={10.1007/978-3-642-02469-6_68} }- Zhongke Gao
Ningde Jin
Year: 2012
Community Structure Detection in Complex Networks with Applications to Gas-Liquid Two-Phase Flow
COMPLEX PART 2
Springer
DOI: 10.1007/978-3-642-02469-6_68
Abstract
We propose an algorithm to detect community structure in complex networks based on data field theory. The efficiency and accuracy of the algorithm for computer-simulated and real networks make it feasible to be used for the accurate detection of community structure in complex networks. Using the conductance fluctuating signals measured from gas-liquid two-phase flow dynamic experiments, we construct the flow pattern complex network. With the applications of the community-detection algorithm to the flow pattern complex network, we achieve good identification of flow pattern in gas-liquid two-phase flow. In this paper, from a new perspective, we not only present a new community-detection algorithm based on data field theory, but also build a bridge between complex network and two-phase flow.