Community Detection On Multi-layer Graph using Intra-layer and Inter-layer Linkage Graphs (CDMIILG)

Abstract

Effective communication in complex networks is crucial for users and organizations, so graph theory is being used to provide solid groundwork for modeling and evaluating these networks. However, users often need help with group splits and communication issues due to ineffective community models. In addressing these limitations, this study proposed a community discovery technique based on Community Detection on Multi-layer Graph using Intra-layer and Inter-layer Linkage Graphs (CDMIILG) to model multiple user relationships. The CDMIILG technique adopts centrality measures, depth-first search models, recursive and transitivity, with the latter playing significant roles in the network. The practicability and effectiveness of the proposed method were evaluated using modern centrality measures and a graph-based machine-learning approach. The model’s performance was evaluated utilizing notable clustering metrics, including Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Modularity Approach, and Statistical Metrics, on seven real-world data sets. The study findings revealed that the proposed model is more effective than existing models for multi-layer graphs by yielding a 2%–7% increment.