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[PDF] Community Structure in Time-Dependent, Multiscale, and Multiplex Networks | Semantic Scholar
Source: semanticscholar.org
Article summary:
1. This article proposes a method for detecting communities in social and biological networks using centrality indices.
2. It tests the method on computer-generated and real-world graphs to determine its sensitivity and reliability.
3. The article also reviews developments in the field, such as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Article analysis:
The article is generally reliable and trustworthy due to its use of scientific methods to test its proposed community detection method on computer-generated and real-world graphs. The results of these tests demonstrate that the method is highly effective at discovering community structure in both types of data. Furthermore, the article provides an overview of developments in this field which are supported by evidence from other sources.
However, there are some potential biases present in the article which should be noted. For example, it does not explore any counterarguments or alternative methods for detecting community structure in networks which could provide a more comprehensive view of this topic. Additionally, it does not discuss any possible risks associated with using this method or any potential drawbacks that could arise from its implementation. Finally, it does not present both sides equally when discussing certain topics such as network correlations or dynamical processes taking place on networks; instead it focuses primarily on one side without exploring any opposing views or perspectives.