Estimated Distance Limited Betweenness Centrality Based on Estimated Betweenness and Approximate Betweenness Algorithms

We propose two kinds of estimated distance limited betweenness (k-BC) algorithm by combining k-BC with two estimated centrality betweenness centrality algorithms – publicly known as estimate betweenness and approximate betweenness. Proposed two algorithms are experimented on seven large real-world datasets and compared against their original algorithms. Experimental result shows that both algorithms converge with k-BC and runs faster. Estimated k-BC based on estimated betweenness algorithm performs better than the other in respect to running time and similarity to k-BC. K-BC values are calculated at least 18–439 times faster by using these estimated k-BC algorithms.