Abstract
Basics of PageRank algorithm have been widely adopted in its variations, tailored for specific scenarios. In this work, we consider the Black Hole metric, an extension of the original PageRank that leverages a (bogus) black hole node to reduce the arc weights normalization effect. We further extend this approach by introducing several black holes to investigate on the cohesiveness of the network, a measure of the strength among nodes belonging to the network. First experiments on real networks show the effectiveness of the proposed approach.
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This work was supported in part by the Piano per la Ricerca 2016/2018 DIEEI Universitá degli Studi di Catania.
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Carchiolo, V., Grassia, M., Longheu, A., Malgeri, M., Mangioni, G. (2019). A PageRank Inspired Approach to Measure Network Cohesiveness. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_33
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