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Titlebook: Community Structure of Complex Networks; Hua-Wei Shen Book 2013 Springer-Verlag Berlin Heidelberg 2013 Community structure.Complex Network

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Source-Synchronous Networks-On-Chipype of identified structure can be directly learned from the network. Our model outperforms the state-of-the-art model in shedding light on various structural regularities of networks, which are beyond the capability of existing models.
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Multiscale Community Detection in Networks with Heterogeneous Degree Distributions, heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our framework. Extensive tests on real world and artificial networks demonstrate that the proposed method possesses high performance at identifying multiscale community structure in heterogeneous networks.
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2190-5053 es the state-of-the-art development of community detection.P.Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major
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https://doi.org/10.1007/978-3-642-76165-2ude community definition, community detection methods, community evolution, measurements for evaluation, and test datasets used in this monograph. We also describe the unresolved problems which deserves much more efforts in the future. This chapter can serve as a brief survey for beginners to the study of community structure in networks.
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Research on the Application of Metaverse Technology in the Field of Intelligent Transportationunities to intelligent transportation, it is also necessary to further improve policy supervision systems, data sharing systems, and information protection mechanisms to prevent potential impacts and risks.
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