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• W2311226186 abstract "Densely connected parts in networks are referred to as communities. Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of true members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., false members of the community). We propose to detect the community from this imperfect and inaccurate set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known." @default.
• W2311226186 created "2016-06-24" @default.
• W2311226186 creator A5023464844 @default.
• W2311226186 date "2016-08-01" @default.
• W2311226186 modified "2023-09-24" @default.
• W2311226186 title "Local community detection as pattern restoration by attractor dynamics of recurrent neural networks" @default.
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• W2311226186 doi "https://doi.org/10.1016/j.biosystems.2016.03.006" @default.
• W2311226186 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27018205" @default.
• W2311226186 hasPublicationYear "2016" @default.
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