Efficient Parallel Ear Decomposition of Graphs with Application to Betweenness-Centrality

Pachorkar, Charudatt; Chaitanya, Meher; Kothapalli, Kishore; Bera, Debajyoti

doi:10.1109/hipc.2016.043

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…Garg et al 32 propose a framework which prune the graph by dead nodes and chain nodes helps in optimization of pagerank computation. In the context of computing centrality measures, one finds this technique used by for example, Pachorkar et al, 13 and Sariyuce et al 33 There have been very few reported parallel algorithms focusing on updating an analytic on dynamic graphs 24 and Ferragina 25 study how to test whether a graph is connected under addition and deletion of edges. They adopt the sparsification framework developed by Eppstein et al 20 In a latter work, Srinisavan et al 22 use the notion of affected vertices to update the shortest paths in a dynamic graph in parallel.…”

Section: Related Workmentioning

confidence: 99%

“…The components, in turn, are connected via articulation points. BCC decomposition has been very popular recently in centrality computations 5,9,13 for its ability to confine the shortest paths between intra-BCC nodes to the same BCC. Using this technique, one can limit the breadth-first traversal (BFS) starting from a node v to the BCC(s) containing v. Doing so reduces the time spent in BFS traversals.…”

Section: Biconnected Component Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The approach of Brandes 3 has been used in several sequential, and parallel algorithms for computing the betweenness-centrality values of nodes in a graph (cf. References 12,13). There have been studies also on how to approximate bc(v) and cc(v) 2,14 using a variety of techniques.…”

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confidence: 99%

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Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Regunta

Tondomker

Shukla

et al. 2021

Concurrency and Computation

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Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. Several studies show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi‐core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this article, we show novel parallel algorithms for updating the betweenness‐ and closeness‐centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness‐ and closeness‐centrality. For the latter, we also introduce techniques based on traversals of the block‐cut tree of a graph. Besides, our algorithms incorporate mechanisms to exploit the structural properties of graphs for enhanced performance. We implement our algorithms on two parallel architectures: an Intel 24‐core CPU and an Nvidia Tesla V100 GPU. To the best of our knowledge, we are the first to show GPU algorithms for the above two problems. In addition, we conduct detailed experiments to study the impact of various parameters associated with our algorithms and their implementation. Our results on a collection of real‐world graphs indicate that our algorithms achieve a significant speedup over corresponding state‐of‐the‐art algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Biconnected Component Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Regunta

Tondomker

Shukla

et al. 2021

Concurrency and Computation

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“…Along the way, we introduce novel and non-trivial pre-processing and post-processing steps that are crucial for obtaining an efficient algorithm for each of the problems. In a recent work, we have used the ear decomposition of a graph to obtain efficient parallel algorithms for computing the betweenness-centrality values at each node of a graph [32].…”

Section: Introductionmentioning

confidence: 99%

Applications of Ear Decomposition to Efficient Heterogeneous Algorithms for Shortest Path/Cycle Problems

Dutta

Chaitanya

Kothapalli

et al. 2018

IJNC

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Graph algorithms play an important role in several fields of sciences and engineering. Prominent among them are the All-Pairs-Shortest-Paths (APSP) and related problems. Indeed there are several efficient implementations for such problems on a variety of modern multi-and manycore architectures.It can be noticed that for several graph problems, parallelism offers only a limited success as current parallel architectures have severe short-comings when deployed for most graph algorithms. At the same time, some of these graphs exhibit clear structural properties due to their sparsity. This calls for particular solution strategies aimed at scalable processing of large, sparse graphs on modern parallel architectures.In this paper, we study the applicability of an ear decomposition of graphs to problems such as all-pairs-shortest-paths and minimum cost cycle basis. Through experimentation, we show that the resulting solutions are scalable in terms of both memory usage and also their speedup over best known current implementations. We believe that our techniques have the potential to be relevant for designing scalable solutions for other computations on large sparse graphs.

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Expediting Parallel Graph Connectivity Algorithms

Kothapalli

Wadwekar

2018

2018 IEEE 25th International Conference on High Performance Computing (HiPC)

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Efficient Parallel Ear Decomposition of Graphs with Application to Betweenness-Centrality

Cited by 11 publications

References 21 publications

Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Applications of Ear Decomposition to Efficient Heterogeneous Algorithms for Shortest Path/Cycle Problems

Expediting Parallel Graph Connectivity Algorithms

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