Abstract
In this paper, by focusing on spatial networks embedded in the real space, we first extend the conventional step-based closeness and betweenness centralities by incorporating inter-nodes link distances obtained from the positions of nodes. Then, we propose a method for accelerating computation of these centrality measures by pruning some nodes and links based on the cut links of a given spatial network. In our experiments using spatial networks constructed from urban streets of cities of several types, our proposed method achieved about twice the computational efficiency compared with the baseline method. Actual amount of reduction in computation time depends on network structures. We further experimentally show by examining the highly ranked nodes that the closeness and betweenness centralities have completely different characteristics to each other.
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References
Boldi, P., Vigna, S.: In-core computation of geometric centralities with hyperball: a hunderd billion nodes and beyond. In: Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW 2013), pp. 621–628 (2013)
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)
Burckhart, K., Martin, O.J.: An interpretation of the recent evolution of the city of Barcelona through the traffic maps. J. Geogr. Inf. Syst. 4(4), 298–311 (2012)
Chakrabarti, S., Dom, B., Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A., Gibson, D., Kleinberg, J.: Mining the web’s link structure. IEEE Comput. 32, 60–67 (1999)
Chierichetti, F., Epasto, A., Kumar, R., Lattanzi, S., Mirrokni, V.: Efficient algorithms for public-private social networks. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), pp. 139–148 (2015)
Cohen, E.: Size-estimation framework with applications to transitive closure and reachability. J. Comput. Syst. Sci. 55, 441–453 (1997)
Cohen, E.: All-distances sketches, revisited: HIP estimators for massive graphs analysis. In: Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 88–99 (2015)
Cohen, E., Delling, D., Pajor, T., Werneck, R.F.: Sketch-based influence maximization and computation: scaling up with guarantees. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 629–638 (2014)
Crucitti, P., Latora, V., Porta, S.: Centrality measures in spatial networks of urban streets. Phys. Rev. E 73(3), 036125 (2006)
Freeman, L.: Centrality in social networks: conceptual clarification. Soc. Netw. 1, 215–239 (1979)
Kimura, M., Saito, K., Ohara, K., Motoda, H.: Speeding-up node influence computation for huge social networks. Int. J. Data Sci. Anal. 1, 1–14 (2016)
Montis, D.A., Barthelemy, M., Chessa, A., Vespignani, A.: The structure of interurban traffic: a weighted network analysis. Environ. Plann. B Plann. Des. 34(5), 905–924 (2007)
Moritz, H.: Geodetic reference system 1980. J. Geodesy 74(1), 128–133 (2000)
Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)
Ohara, K., Saito, K., Kimura, M., Motoda, H.: Resampling-based framework for estimating node centrality of large social network. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 228–239. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11812-3_20
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)
Park, K., Yilmaz, A.: A social network analysis approach to analyze road networks. In: Proceedings of the ASPRS Annual Conference (2010)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Super mediator - a new centrality measure of node importance for information diffusion over social network. Inf. Sci. 329, 985–1000 (2016)
Wang, P., Hunter, T., Bayen, A.M., Schechtner, K., Gonzalez, M.C.: Understanding Road Usage Patterns in Urban Areas. Scientific Reports 2 (2012)
Acknowledgments
This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award number FA2386-16-1-4032, and JSPS Grant-in-Aid for Scientific Research (C) (No. 26330261).
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Ohara, K., Saito, K., Kimura, M., Motoda, H. (2016). Accelerating Computation of Distance Based Centrality Measures for Spatial Networks. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_24
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DOI: https://doi.org/10.1007/978-3-319-46307-0_24
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