Community Detection in Social Networks based on Local Edge Centrality and Adaptive Thresholding
Detecting communities in a network are crucial to understand the network structures and to analyze the network characteristics. Considering the significance of edges, a centrality measure is proposed based on the local perspective referred to as the Local Edge Centrality. The proposed approach makes use of the divisive method wherein links having a LEC value that is greater than a chosen threshold is progressively deleted to obtain an initial partition. Linearly spaced thresholds are applied to the network and topological characteristics are analyzed. The point at which there is a drastic shift in the topological properties represents the ideal threshold which is used to obtain the initial partition with better modularity. Then modularity optimization is employed to obtain the final partition of the network.
2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)