This paper presents a faster algorithm for computing betweenness centrality in social networks. Betweenness centrality is a key measure in social network analysis, but existing algorithms are computationally expensive, requiring Θ(n³) time and Θ(n²) space. The authors propose new algorithms that require O(n + m) space and run in O(nm) and O(nm + n² log n) time for unweighted and weighted networks, respectively, where n is the number of actors and m is the number of links. These improvements significantly increase the range of networks for which centrality analysis is feasible. The paper introduces a new accumulation technique that integrates well with traversal algorithms for solving the single-source shortest-paths problem, exploiting the sparsity of typical networks. This technique allows for the efficient computation of betweenness centrality, as well as other centrality indices based on shortest paths, reducing both time and space requirements. The algorithm works by first computing the number of shortest paths between all pairs of vertices. This is done using traversal algorithms such as BFS for unweighted graphs and Dijkstra's algorithm for weighted graphs. The number of shortest paths is then used to compute the pair-dependencies, which are summed to determine the betweenness centrality of each vertex. The authors also show that the new algorithm can be used to compute other centrality indices, such as closeness, graph, and stress centrality, simultaneously. This reduces the overall time and space complexity of comparative analyses. The algorithm is implemented for both directed and undirected graphs and is tested on real and randomly generated data. The results show that the new algorithm is significantly faster than existing methods, especially for large and sparse networks. The algorithm is also implemented in the publicly available network analysis tool Pajek and has been shown to be effective on large networks with thousands of actors. The results demonstrate that the new algorithm can compute betweenness centrality for large networks efficiently, even when the network is sparse.This paper presents a faster algorithm for computing betweenness centrality in social networks. Betweenness centrality is a key measure in social network analysis, but existing algorithms are computationally expensive, requiring Θ(n³) time and Θ(n²) space. The authors propose new algorithms that require O(n + m) space and run in O(nm) and O(nm + n² log n) time for unweighted and weighted networks, respectively, where n is the number of actors and m is the number of links. These improvements significantly increase the range of networks for which centrality analysis is feasible. The paper introduces a new accumulation technique that integrates well with traversal algorithms for solving the single-source shortest-paths problem, exploiting the sparsity of typical networks. This technique allows for the efficient computation of betweenness centrality, as well as other centrality indices based on shortest paths, reducing both time and space requirements. The algorithm works by first computing the number of shortest paths between all pairs of vertices. This is done using traversal algorithms such as BFS for unweighted graphs and Dijkstra's algorithm for weighted graphs. The number of shortest paths is then used to compute the pair-dependencies, which are summed to determine the betweenness centrality of each vertex. The authors also show that the new algorithm can be used to compute other centrality indices, such as closeness, graph, and stress centrality, simultaneously. This reduces the overall time and space complexity of comparative analyses. The algorithm is implemented for both directed and undirected graphs and is tested on real and randomly generated data. The results show that the new algorithm is significantly faster than existing methods, especially for large and sparse networks. The algorithm is also implemented in the publicly available network analysis tool Pajek and has been shown to be effective on large networks with thousands of actors. The results demonstrate that the new algorithm can compute betweenness centrality for large networks efficiently, even when the network is sparse.