Abstract
Variety of real-life structures can be simplified by a graph. Such simplification emphasizes the structure represented by vertices connected via edges. A common method for the analysis of the vertices importance in a network is betweenness centrality. The centrality is computed using the information about the shortest paths that exist in a graph. This approach puts the importance on the edges that connect the vertices. However, not all vertices are equal. Some of them might be more important than others or have more significant influence on the behavior of the network. Therefore, we introduce the modification of the betweenness centrality algorithm that takes into account the vertex importance. This approach allows the further refinement of the betweenness centrality score to fulfill the needs of the network better. We show this idea on an example of the real traffic network. We test the performance of the algorithm on the traffic network data from the city of Bratislava, Slovakia to prove that the inclusion of the modification does not hinder the original algorithm much. We also provide a visualization of the traffic network of the city of Ostrava, the Czech Republic to show the effect of the vertex importance adjustment. The algorithm was parallelized by MPI (http://www.mpi-forum.org/) and was tested on the supercomputer Salomon (https://docs.it4i.cz/) at IT4Innovations National Supercomputing Center, the Czech Republic.
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Acknowledgements
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project ‘IT4Innovations excellence in science—LQ1602’, by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center—LM2015070”, and partially by ANTAREX, a project supported by the EU H2020 FET-HPC program under grant 671,623, and by grant of SGS No. SP2017/182 “Solving graph problems on spatio-temporal graphs with uncertainty using HPC”, VŠB—Technical University of Ostrava, Czech Republic.
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Hanzelka, J., Běloch, M., Martinovič, J., Slaninová, K. (2019). Vertex Importance Extension of Betweenness Centrality Algorithm. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-13-1402-5_5
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DOI: https://doi.org/10.1007/978-981-13-1402-5_5
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