Abstract
Centrality measures are crucial in quantifying the roles and positions of vertices in complex network analysis. An important and popular measure is betweenness centrality, which is computed based on the number of shortest paths that vertices fall on. However, betweenness is computationally expensive to derive, resulting in much research on efficient computation techniques. We note that in many applications, it is the set of vertices with high betweenness that is of key interest and that their betweenness rankings rather than the exact values is usually adequate for analysts to work with. Hence, we have developed a novel algorithm that efficiently returns the set of vertices with highest betweenness. The convergence criterion for our algorithm is based on the membership stability of the high-betweenness set. Through experiments on various artificial and real-world networks, we show that the algorithm is both efficient and accurate. From the experiments, we also demonstrated that the algorithm tends to perform better on networks with heterogeneous betweenness distributions.
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Chong, W.H., Toh, W.S.B., Teow, L.N. (2013). Efficient Extraction of High-Betweenness Vertices from Heterogeneous Networks. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds) The Influence of Technology on Social Network Analysis and Mining. Lecture Notes in Social Networks, vol 6. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1346-2_11
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DOI: https://doi.org/10.1007/978-3-7091-1346-2_11
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