Abstract
With the rapid expansion of communication data, research about analyzing social networks has become a hotspot. Finding the shortest path (SP) in social networks can help us to investigate the potential social relationships. However, it is an arduous task, especially on large-scale problems. There have been many previous studies on the SP problem, but very few of them considered the peculiarity of social networks. This paper proposed a community structure based method to accelerate answering the SP problem of social networks during online queries. We devise a two-stage strategy to strike a balance between offline pre-computation and online consultations. Our goal is to perform fast and accurate online approximations. Experiments show that our method can instantly return the SP result while satisfying accuracy constraint.
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Acknowledgments
This work was supported in part by the National Nature Science Foundation of China under the grants 61702285 and 61772289, the Natural Science Foundation of Tianjin under the grants 17JCQNJC00200, and the Fundamental Research Funds for the Central Universities under the grants 63181317.
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Chai, Y., Song, C., Nie, P., Yuan, X., Ge, Y. (2018). Community Structure Based Shortest Path Finding for Social Networks. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_19
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DOI: https://doi.org/10.1007/978-3-319-98809-2_19
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