Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

Abstract

Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.

Strategies for improving the quality of community detection based on modularity optimization

“…Optimal objective function when maximum modularity and inertial are achieved. Singh et al [87] proposed a new objective function as a combination of the Louvain-and-attribut and Louvain-or_attribut methods, which combines Louvain modularity with dependence on similarity attributes and considers irrelevant attributes (outlier). A different approach is taken in combining the structure with node attributes with mathematical programming [88], and with spectral clustering [89].…”