Efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs

Shukla, Kshitij; Regunta, Sai Charan; Tondomker, Sai Harsh; Kothapalli, Kishore

doi:10.1145/3392717.3392743

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…Even in this case, the focus is on changes related to one single edge. The solution proposed in [22], instead, handles batches of updates in parallel. In particular, it exploits a bi-connected component decomposition technique along with some structural properties to improve performance.…”

Section: Related Workmentioning

confidence: 99%

Fast cluster-based computation of exact betweenness centrality in large graphs

et al. 2021

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Nowadays a large amount of data is originated by complex systems, such as social networks, transportation systems, computer and service networks. These systems can be modeled by using graphs and studied by exploiting graph metrics, such as betweenness centrality (BC), a popular metric to analyze node centrality of graphs. In spite of its great potential, this metric requires long computation time, especially for large graphs. In this paper, we present a very fast algorithm to compute BC of undirected graphs by exploiting clustering. The algorithm leverages structural properties of graphs to find classes of equivalent nodes: by selecting one representative node for each class, we are able to compute BC by significantly reducing the number of single-source shortest path explorations adopted by Brandes’ algorithm. We formally prove the graph properties that we exploit to define the algorithm and present an implementation based on Scala for both sequential and parallel map-reduce executions. The experimental evaluation of both versions, conducted with synthetic and real graphs, reveals that our solution largely outperforms Brandes’ algorithm and significantly improves known heuristics.

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Section: Related Workmentioning

confidence: 99%

Fast cluster-based computation of exact betweenness centrality in large graphs

et al. 2021

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“…Up to this point, research was conducted to decrease the runtime of the centrality algorithms. In [17], the authors propose parallel versions of betweenness and closeness centrality that can handle dynamic graphs, where nodes and edges could change in every time step. To access this problem they process a batch of updates in a parallel way.…”

Section: Related Workmentioning

confidence: 99%

An Experimental Study on Centrality Measures Using Clustering

2021

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Graphs can be found in almost every part of modern life: social networks, road networks, biology, and so on. Finding the most important node is a vital issue. Up to this date, numerous centrality measures were proposed to address this problem; however, each has its drawbacks, for example, not scaling well on large graphs. In this paper, we investigate the ranking efficiency and the execution time of a method that uses graph clustering to reduce the time that is needed to define the vital nodes. With graph clustering, the neighboring nodes representing communities are selected into groups. These groups are then used to create subgraphs from the original graph, which are smaller and easier to measure. To classify the efficiency, we investigate different aspects of accuracy. First, we compare the top 10 nodes that resulted from the original closeness and betweenness methods with the nodes that resulted from the use of this method. Then, we examine what percentage of the first n nodes are equal between the original and the clustered ranking. Centrality measures also assign a value to each node, so lastly we investigate the sum of the centrality values of the top n nodes. We also evaluate the runtime of the investigated method, and the original measures in plain implementation, with the use of a graph database. Based on our experiments, our method greatly reduces the time consumption of the investigated centrality measures, especially in the case of the Louvain algorithm. The first experiment regarding the accuracy yielded that the examination of the top 10 nodes is not good enough to properly evaluate the precision. The second experiment showed that the investigated algorithm in par with the Paris algorithm has around 45–60% accuracy in the case of betweenness centrality. On the other hand, the last experiment resulted that the investigated method has great accuracy in the case of closeness centrality especially in the case of Louvain clustering algorithm.

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“…Graphs are a useful abstraction to model a variety of phenomena from domains such as social networks, 1,2 biological networks, 3 and transportation networks. A significant problem with graphs from these domains is computing the various centrality metrics.…”

Section: Introductionmentioning

confidence: 99%

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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Efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs

Cited by 12 publications

References 29 publications

Fast cluster-based computation of exact betweenness centrality in large graphs

Fast cluster-based computation of exact betweenness centrality in large graphs

An Experimental Study on Centrality Measures Using Clustering

Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

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