Abstract
In our paper we compare two centrality measures of networks, namely betweenness and Linerank. Betweenness is a popular, widely used measure, however, its computation is prohibitively expensive for large networks, which strongly limits its applicability in practice. On the other hand, the calculation of Linerank remains manageable even for graphs of billion nodes, therefore it was offered as a substitute of betweenness in [4]. Nevertheless, to the best of our knowledge the relationship between the two measures has never been seriously examined. As a first step of our experiments we calculate the Pearson’s and Spearman’s correlation coefficients for both the node and edge variants of these measures. In the case of the edges the correlation is varying but tends to be rather low. Our tests with the Girvan-Newman algorithm for detecting clusters in networks [7] also underlie that edge betweenness cannot be substituted with edge Linerank in practice. The results for the node variants are more promising. The correlation coefficients are close to 1 almost in all cases. Notwithstanding, in the practical application in which the robustness of social and web graphs to node removal is examined node betweenness still outperforms node Linerank, which shows that even in this case the substitution still remains a problematic issue. Beside these investigations we also clarify how Linerank should be computed on undirected graphs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adamic, L.A., Glance, N.: The political blogosphere and the 2004 u.s. election: Divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, pp. 36–43. ACM (2005)
Boldi, P., Rosa, M., Vigna, S.: Robustness of social and web graphs to node removal. Social Netw. Analys. Mining 3(4), 829–842 (2013)
Fortunato, S., Lancichinetti, A.: Community detection algorithms: A comparative analysis: Invited presentation, extended abstract. In: Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2009, pp. 27:1–27:2. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2009)
Kang, U., Papadimitriou, S., Sun, J., Tong, H.: Centralities in large networks: Algorithms and observations. In: SDM, pp. 119–130. SIAM / Omni Press (2011)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4) (2008)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1) (2007)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E, 69(2) (2004)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. In: Proceedings of the 7th International World Wide Web Conference, pp. 161–172 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kósa, B., Balassi, M., Englert, P., Kiss, A. (2014). Betweenness versus Linerank. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-11289-3_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11288-6
Online ISBN: 978-3-319-11289-3
eBook Packages: Computer ScienceComputer Science (R0)