Performance Evaluation of Betweenness Centrality Using Clustering Methods

Szabari, Bence; Kiss, Attila

doi:10.24193/subbi.2020.1.05

Cited by 2 publications

(3 citation statements)

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“…Due to the loss of the edges between sub-graphs, our algorithm only gives an approximate solution of the measures compared to the calculation on the whole graph; therefore, the centrality values calculated by our method might differ from the values obtained by centrality algorithms on the complete graph. Our experiments yielded that the proposed algorithm in [20] is accurate in the case of closeness centrality, scales well, and is able to determine influential nodes up to 20 times faster than traditional centrality measures.…”

Section: The Algorithmmentioning

confidence: 90%

“…In [19], they propose a lossy graph reduction approach that reduces the execution time of the centrality algorithms. After our investigations in this field, we decided to extend our previous research [20] and investigate the ranking efficiency and execution time of more centrality measures using clustering methods. Markov clustering [21] is a popular algorithm that is commonly used to cluster protein sequences in bioinformatics data [22].…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we explain our algorithm that we proposed in [20] and is used in our experiments. Let G be an undirected graph with N nodes.…”

Section: The Algorithmmentioning

confidence: 99%

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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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Section: The Algorithmmentioning

confidence: 90%