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Abstract
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Networks are used to represent associations among entities in many fields [1]. Multilayer social networks are the main representative form of today’s social networks. In fact, the multiplicity of relations, the huge amount of data, and the dynamic nature of nowadays social networks impose the representation of the network with multiple layers. This new representation makes network analysis more challenging especially Community retrieval. So, researchers propose different approaches to handle these challenges to detect accurate communities in the multilayer networks. Discovering communities in multilayer networks differs from single layer networks. In fact, densely connected groups of nodes exist in different layers. Therefore, revealing these communities necessitates new approaches and methods to deal with the complexity of the multilayer networks and especially to deal with the multiple views of the network [2]. For influence maximization, Influential Nodes are found in communities. community detection and Identifying Influential Node algorithms reduce the computational overhead by using community structure. In this algorithm, seed nodes are selected using nodes that have high power. Also, these nodes have a high correlation with influential nodes in other communities, which causes optimal diffusion in social networks. As a result, a set of seed nodes is selected by calculating the influence spread, which increases the algorithm's accuracy [3].
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