Computing and Informatics

With the increasing diversity and complexity of online social networks, effectively dividing communities presents a growing challenge. These networks are characterized by their large scale, sparse structure, and numerous isolated points. Traditional community detection methods lack consideration of node attribute information, thereby negatively impacting the accuracy of community detection. To address these challenges, this paper presents a novel Louvain-FTAS community detection algorithm that integrates topology and attribute structure. The proposed algorithm first selects attributes with positive effects to account for attribute heterogeneity. Subsequently, it utilizes a semi-local strategy to calculate topology similarity and information entropy to calculate attribute similarity. These values are combined to obtain the final node similarity matrix, which is then fed into the Louvain algorithm to maximize modularity and incorporate multi-dimensional attribute features to enhance community detection accuracy. The proposed model is evaluated through comparative experiments on two real datasets and artificial synthetic networks, demonstrating its rationality and effectiveness.