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Clustering helps in understanding the patterns present in networks and thus helps in getting useful insights. In real‐world complex networks, analysing the structure of the network plays a vital role in clustering. Most of the existing clustering algorithms identify disjoint clusters, which do not consider the structure of the network. Moreover, the clustering results do not provide consistency and...
Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and...
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