A Round-Efficient Distributed Betweenness Centrality Algorithm
We present Min-Rounds BC (MRBC), a distributed-memory algorithm in the CONGEST model that computes the betweenness centrality (BC) of every vertex in a directed unweighted n-node graph in O (n) rounds. Min-Rounds BC also computes all-pairs-shortest-paths (APSP) in such graphs. It improves the number of rounds by at least a constant factor over previous results for unweighted directed APSP and for unweighted BC, both directed and undirected.
We implemented MRBC in D-Galois, a state-of-the-art distributed graph analytics system, incorporated additional optimizations enabled by the D-Galois model, and evaluated its performance on a production cluster with up to 256 hosts using power-law and road networks. Compared to the BC algorithm of Brandes, on average, MRBC reduces the number of rounds by 14.0× and the communication time by 2.9× for the graphs in our test suite. As a result, MRBC is 2.1× faster on average than Brandes BC for real-world web-crawls on 256 hosts.
Tue 19 FebDisplayed time zone: Guadalajara, Mexico City, Monterrey change
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