Edge Similarity Index for Complex Network Analysis


  • Natarajan Meghanathan Jackson State University


Edge Similarity, Centrality Metrics, Threshold Distance, Binary Search Algorithm


We propose a novel network-level metric called Edge Similarity Index (ESI) to quantify the extent of similarity between any two edges of a complex network with respect to the values for a node-level metric (like centrality metric) of its end vertices. To assess the ESI measure for a complex real-world network with respect to a node-level metric, we propose to first construct a logical network whose vertices are the actual edges of the network (with coordinates corresponding to the normalized node-level metric values of the actual end vertices) and there exists a (logical) edge between two logical vertices if the Euclidean distance between their corresponding coordinates is within a threshold distance. We propose a binary search algorithm to determine the minimum value for this threshold distance () that would result in a connected logical unit-disk graph; the ESI value for the complex network is then computed as . The ESI values range from 0.0 to 1.0; the larger the ESI value with respect to a node-level metric, we claim that more similar are any two edges in the network with respect to the node-level metric values for their end vertices.




How to Cite

Meghanathan, N. (2020). Edge Similarity Index for Complex Network Analysis. International Journal of Combinatorial Optimization Problems and Informatics, 11(3), 76–96. Retrieved from https://ijcopi.org/ojs/article/view/129