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. © 2009 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
CITATION STYLE
Gao, Z., & Jin, N. (2009). Community structure detection in complex networks with applications to gas-liquid two-phase flow. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (Vol. 5 LNICST, pp. 1917–1928). https://doi.org/10.1007/978-3-642-02469-6_68
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