Nearly Optimal Distributed Algorithm for Computing Betweenness Centrality

Hua, Qiang-Sheng; Fan, Haoqiang; Ai, Ming; Qian, Lixiang; Li, Yangyang; Shi, Xuanhua; Jin, Hai

doi:10.1109/icdcs.2016.89

Cited by 10 publications

(25 citation statements)

References 17 publications

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“…Our new accumulation technique is a general method that works for any distributed BC algorithm where each node can keep track of the round in which step 4 of Algorithm 1 is finalized for each source. This is the case not only for Algorithm 3 for both directed and undirected unweighted graphs, but also for the BC algorithm in [31] for undirected unweighted graphs (though our Alg. 3 uses a smaller number of rounds).…”

Section: Accumulation Technique and Bc Computationmentioning

confidence: 97%

“…At the same time we also improve the round and/or message complexity (by a constant factor) for APSP in both undirected and directed graphs and for BC in undirected graphs. Prior to our work, the best previous CONGEST algorithms for unweighted APSP were in [38] and the only nontrivial CON-GEST algorithm for BC was the undirected unweighted BC algorithm in [31].…”

Section: Congest Modelmentioning

confidence: 99%

“…In the case when exponential numbers of shortest paths exist in the graph, we can use the approximation technique introduced in [31] which uses only O (log n)-size messages and computes a provably good approximation of the BC values.…”

Section: Our Contributionsmentioning

confidence: 99%

“…There are other O (n)-round undirected APSP algorithms [29,48] but these require bidirectional edges and do not work for directed graphs (for example, the use of distances along a pebble traversal of a BFS tree in the proof of Lemma 1 in [29]). Similarly, the undirected BC algorithm in [31] does not work for directed graphs even if we substitute a directed APSP algorithm since their method for the accumulation phase is tied to the undirected APSP method in [29].…”

Section: Our Contributionsmentioning

confidence: 99%

“…For Betweenness Centrality, an O (n)-round CONGEST algorithm for undirected unweighted graphs was given in [31], along with an Ω( n log n + D u )-round lower bound and a method to approximate an exponential number of shortest paths with log-size messages. An approximation algorithm for computing random walk BC in O (n log n) rounds in the CONGEST model was recently given in [30].…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

A round-efficient distributed betweenness centrality algorithm

Hoang

Pontecorvi

Dathathri

et al. 2019

Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

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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 2.8× 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.

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Section: Accumulation Technique and Bc Computationmentioning

confidence: 97%

Section: Congest Modelmentioning

confidence: 99%

Section: Our Contributionsmentioning

confidence: 99%

Section: Our Contributionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A round-efficient distributed betweenness centrality algorithm

Hoang

Pontecorvi

Dathathri

et al. 2019

Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

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A nearly optimal distributed algorithm for computing the weighted girth

Hua

Qian

et al. 2021

Sci. China Inf. Sci.

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Computing the weighted girth, which is the sum of weights of edges in the minimum weight cycle, is an important problem in network analysis. The problem for distributively computing girth in unweighted graphs has garnered lots of attention, but there are few studies in weighted graphs. In this paper, we propose a distributed randomized algorithm for computing the weighted girth in weighted graphs with integral edge weights in the range [1, n c ], where n is the number of vertices and c is a constant. The algorithm is devised under the standard synchronous CON GEST model, which limits each vertex can only transfer O(log n) bits information along each incident edge in a round. The upper bound of the algorithm is O(n log 2 n) rounds. We also prove the lower bound for computing the weighted girth is Ω(D + n/ log n) where D is the hop diameter of the weighted graph. This means our distributed algorithm is optimal within a factor of O(log 3 n).

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Distributively Computing Random Walk Betweenness Centrality in Linear Time

Hua

Jin

et al. 2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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Nearly Optimal Distributed Algorithm for Computing Betweenness Centrality

Cited by 10 publications

References 17 publications

A round-efficient distributed betweenness centrality algorithm

A round-efficient distributed betweenness centrality algorithm

A nearly optimal distributed algorithm for computing the weighted girth

Distributively Computing Random Walk Betweenness Centrality in Linear Time

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