By a News Reporter-Staff News Editor at Physics Week -- Investigators discuss new findings in Statistical Mechanics. According to news reporting originating from Changsha, People’s Republic of China, by VerticalNews correspondents, research stated, “Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization.”
Funders for this research include National Natural Science Foundation of China, Scientific Research Fund of Education Department of Hunan Province, Scientific Research Project of Hunan Provincial Health and Family Planning Commission of China, Department of Education of Hunan Province, Hunan Provincial Natural Science Foundation of China, National Social Science Foundation of China.
Our news editors obtained a quote from the research from the Department of Computer Science, “Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection.”
According to the news editors, the research concluded: “The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.”
For more information on this research see: Adaptive multi-resolution Modularity for detecting communities in networks. Physica A-Statistical Mechanics and Its Applications , 2018;491():591-603. Physica A-Statistical Mechanics and Its Applications can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands.
The news editors report that additional information may be obtained by contacting S. Chen, Changsha Med Univ, Dept. of Comp Sci, Changsha 410219, Hunan, People’s Republic of China. Additional authors for this research include Z.Z. Wang, M.H. Bao, L. Tang, J. Zhou, J. Xiang, J.M. Li and C.H. Yi.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.physa.2017.09.023. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-01-23), Studies from Department of Computer Science Have Provided New Data on Statistical Mechanics (Adaptive multi-resolution Modularity for detecting communities in networks), Physics Week, 629, ISSN: 1944-2661, BUTTER® ID: 015049142
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