Semantic Relation-Based Modularity-Optimized Community Detection Algorithm for Heterogeneous Networks

Abstract

AbstractThis paper presents the semantic relation-based modularity-optimized community detection algorithm for Heterogeneous Networks. This paper aims to try to increase the modularity value of the network by using the content analysis of the network and the link analysis together. Therefore, similarity values between people's shares were calculated and included in the network as indirect links. The proposed content- and link-based methods are a greedy hierarchical clustering algorithm that uses indirect connections with the network structure and ensures that the nodes most relative to each other are topologically and semantically grouped with priority. This paper presents a comparative analysis to analyze the impact of semantic relation over the optimization algorithm, i.e. Parliamentary Optimization Algorithm (POA) and Modularity Optimization Algorithm (MOA) for community detection. Finally, the modularity and NMI of the proposed work were evaluated over six real network-based heterogeneous network data sets and gained a satisfactory modularity rate over the resultant informative community.KeywordsSocial mediaCommunity detectionClusteringOptimization algorithmModularitySemantic relationHierarchical clustering algorithmSeed community