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Hybrid modeling approach for contextualized community detection in multilayer social network: emergency management case study

机译:多层社交网络中上下文社区检测的混合建模方法:应急管理案例研究

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Within multilayer social networks, finding relevant communities for each specific situation has been a challenging task. Thus, modeling these social networks is the key issue for the process of contextualized community detection. However, traditional formalisms for representing multilayer social networks suffer from the lack of semantics. In the scope of this paper, we propose a hybrid modeling approach to represent participants and community detection context in multilayer social network. This approach combines a semantically rich description of social data (Ontology-based model) with a powerful mathematical abstraction (Graph-based model). Furthermore, we present a modeling scenario in the field of emergency management to illustrate how the proposed model can be used to contextualize community detection within a real social network. Finally, a comparison with another modeling approach is given in order to evaluate the proposed model performance.
机译:在多层社交网络中,为每种特定情况找到相关社区一直是一项艰巨的任务。因此,对这些社交网络进行建模是上下文化社区检测过程的关键问题。但是,代表多层社交网络的传统形式主义缺乏语义。在本文的范围内,我们提出了一种混合建模方法来表示参与者和多层社交网络中的社区检测上下文。这种方法将语义丰富的社交数据描述(基于本体的模型)与强大的数学抽象(基于图形的模型)结合在一起。此外,我们在应急管理领域提出了一种建模方案,以说明所提出的模型如何用于在真实的社交网络中对社区检测进行情境化。最后,与另一种建模方法进行了比较,以评估所提出的模型性能。

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