Technical perspective: Graphs, betweenness centrality, and the GPU
نویسندگان
چکیده
منابع مشابه
Further Results on Betweenness Centrality of Graphs
Betweenness centrality is a distance-based invariant of graphs. In this paper, we use lexicographic product to compute betweenness centrality of some important classes of graphs. Finally, we pose some open problems related to this topic.
متن کاملComputing Betweenness Centrality for Small World Networks on a GPU
Although a graphics processing unit (GPU) is a specialized device tailored primarily for compute-intensive, highly dataparallel computations; significant acceleration can be achieved on memory-intensive graph algorithms as well. In this work, we investigate the performance of a graph algorithm for computing vertex betweenness centrality for small world networks on 2 NVIDIA Tesla and Fermi GPUs ...
متن کاملFully Dynamic Betweenness Centrality
We present fully dynamic algorithms for maintaining betweenness centrality (BC) of vertices in a directed graph G = (V,E) with positive edge weights. BC is a widely used parameter in the analysis of large complex networks. We achieve an amortized O(ν∗ · log n) time per update with our basic algorithm, and O(ν∗ · log n) time with a more complex algorithm, where n = |V |, and ν∗ bounds the number...
متن کاملEfficient algorithms for updating betweenness centrality in fully dynamic graphs
Betweenness centrality of a vertex (edge) in a graph is a measure for the relative participation of the vertex (edge) in the shortest paths in the graph. Betweenness centrality is widely used in various areas such as biology, transportation, and social networks. In this paper, we study the update problem of betweenness centrality in fully dynamic graphs. The proposed update algorithm substantia...
متن کاملBetweenness Centrality - Incremental and Faster
We present an incremental algorithm that updates the betweenness centrality (BC) score of all vertices in a graph G when a new edge is added to G, or the weight of an existing edge is reduced. Our incremental algorithm runs in O(ν∗ · n) time, where ν∗ is bounded by m∗, the number of edges that lie on a shortest path in G. We achieve the same bound for the more general incremental vertex update ...
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ژورنال
عنوان ژورنال: Communications of the ACM
سال: 2018
ISSN: 0001-0782,1557-7317
DOI: 10.1145/3230483