A type-2 fuzzy community detection model in large-scale social networks considering two-layer graphs.pdf (306.24 kB)

A type-2 fuzzy community detection model in large-scale social networks considering two-layer graphs

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posted on 23.08.2020 by Susan Bastani, Mansoureh Naderipour, Mohammad Hossein Fazel Zarandi
This paper mainly aims to identify communities with different interactions between nodes in complex networks. Community detection algorithms partition vertices into denselyconnected components in a complex network. In recent researches, a node is related to multiple aspects of relationships resulting in new challenges in social networks. The two aspects of relationships could be shown as a two-layer graph which comprises two graphs dependent on each other; and each graph shows a specific aspect of the interaction. In this research, a new community detection model is proposed based on the possibilistic cmeans clustering model considering two-layer graphs (PCMTL) in order to detect overlapping communities based on the two-layer graphs using both structural and attribute similarities in large-scale social networks. The nodes are assigned to communities by upper and lower membership values that are indicative of the degree of belonging to the communities through type-2 fuzzy membership values, and the suggested values of interval type-2 fuzzy membership determine how a node belongs to a community with regard to two different aspects of interactions in a two-layer graph. Moreover, according to the proposed model, a validity index is introduced to assess the suggested model in comparison to the approach existing in the literature. Ultimately, two artificial and two real large-scale social networks are used to validate the performance of the suggested model.

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