A novel local approach for identifying bridging edges in complex networks

Detecting the bridging edges is a fundamental issue in complex networks, for investigating the network connectivity, modularity, immunization, and percolation. However, less attention has been paid to studying the edge importance. Also, few available edge centrality measures, are suffering from low accuracy in distinguishing the vital edges, and have high time complexity. Considering these issues, in this paper we propose a novel edge centrality measure for detecting bridging edges by utilizing local neighborhood information of the edges. The negative effect of the alternative paths between the ending nodes of the corresponding edge is also considered in the proposed formula. Experimental results indicate the superior performance of the approach in all the real-world datasets.