KeyGraph-based community detection algorithm for public security intelligence A KeyGraph-based community detection algorithm (KCD) was put forward in order to cluster the characteristics of human behavior, so as to detect the crowds with similar properties and classify them to provide decision support for the department of public security intelligence. KCD was proceeded from the features of human behavior; the identification of relational crowds was realized through establishing KeyGraph and employing graph cluster algorithms. Firstly, the multi-dimension behavior features between human behaviors were quantified, and quantized features were merged to generate the co-occurrence set in the form of a triad: “people-people-value”. Then noise data were filtered and the undirected graph based on the characteristics of human behavior was established. Finally graph-clustering algorithm SCAN was applied to find out a number of different groups on undirected graph, where hubs and outliers were also located. As results, KCD could solve the problem of detecting the key persons among communities in the context of public security intelligence.

References in zbMATH (referenced in 8 articles )

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  1. Han, Jingyu; Chen, Kejia; Wang, Jianing: Web article quality ranking based on web community knowledge (2015)
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  3. Hong, Chao-Fu: Qualitative chance discovery - extracting competitive advantages (2009) ioport
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  6. Sakakibara, Tsuneki; Ohsawa, Yukio: Gradual-increase extraction of target baskets as preprocess for visualizing simplified scenario maps by KeyGraph (2007) ioport
  7. Matsumura, Naohiro; Ohsawa, Yukio; Ishizuka, Mitsuru: PAI: Automatic indexing for extracting asserted keywords from a document (2003)
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