Outer Betweenness Centrality exploits parallelism using the former approach.ĭifferent threads execute single shortest-path computations Additionally,Įach of the shortest path computations can be done in parallel. The shortest path exploration from each source node in parallel. This algorithm has parallelism at multiple levels. The centrality value of a node \(v\), we finally compute the sum Graphs, and computes the betweenness centrality scoreįor all vertices in the graph in \(O(VE V^2 \log(v)\) for all other \(v \in V\). īrandes's algorithm exploits the sparse nature of typical real-world Our parallel implementations are based on Brandes's algorithm. We have two different implementations: one that we term outer betweenness centralityĪnd the BC algorithm presented by Dimitrios Prountzos. The benchmark takes as input a directed graph and returns the betweenness The betweenness centrality of \(u\) is the sum of its betweenness scores forĪll possible pairs of \(s\) and \(t\) in the graph. Percentage of shortest paths between \(s\) and \(t\) that include \(u\). Let \(G=(V,E)\) be a graph and let \(s,t\)īe a fixed pair of graph nodes.The betweenness score of a node \(u\) is the Betweenness centrality is a shortest path enumeration-based This benchmark computes the betweenness centrality of each node in a network,Ī metric that captures the importance of each individual node in the overall Betweenness Centrality Application Description
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