Abstract
A large network frequently evolves in nature; hence, it is necessary to update the betweenness centrality efficiently. Previously, the complexity for updated betweenness centrality is found \(O(n^3)\), where n is the number of the nodes in the network. It is hard to find a network with static size. To calculate, betweenness centrality for the evolving network is a nontrivial task due to high complexity. The betweenness centrality of all the nodes should be recomputed. Brendes proposed an algorithm for calculating updated betweenness centrality for the static networks with complexity, O(nm), where m is the total number of edges. A method QUBE was proposed which efficiently reduces the search space by finding candidate set of nodes and compute the betweenness centrality of the candidate nodes whose betweenness centrality need to be updated when any edge/node is inserted/deleted in the network. We have proposed a new algorithm which updates the betweenness centrality of a candidate set of nodes only with the approximation method. In the present investigations, a method is suggested to recompute the betweenness centrality for k number of nodes in the network, \((k < n)\), without actually recomputing the betweenness centrality for all the nodes. It is found that the time complexity of proposed algorithm is lower than the existing algorithms.
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Kumari, P., Singh, A. (2019). Approximation and Updation of Betweenness Centrality in Dynamic Complex Networks. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_3
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DOI: https://doi.org/10.1007/978-981-13-1132-1_3
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