Abstract
Given two vertices in a graph, computing their distance is a fundamental operation over graphs. However, classical exact methods for this problem often cannot scale up to the rapidly evolving graphs in recent applications. Many approximate methods have been proposed, including some landmark-based methods that have been shown to have good scalability and estimate the upper bound of the distance in acceptable accuracy. In this paper, we propose a new landmark-based framework based a new measure called coverage to more accurately estimate the lower bound of the distance. Although we can prove that selecting the optimal set of landmarks is NP-hard, we propose a heuristic algorithm that can guarantee the approximation ratio. Furthermore, we implement our method through the distributed graph processing systems while considering the characteristic of the distributed graph processing systems. Experiments on large real graphs confirm the superiority of our methods.
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Acknowledgment
The corresponding author is Peng Peng, and this work was supported by NSFC under grant 61702171, 61772191 and 61472131, Hunan Provincial Natural Science Foundation of China under grant 2018JJ3065, the Fundamental Research Funds for the Central Universities, Science and Technology Key Projects of Hunan Province (Grant No. 2015TP1004, 2016JC2012), and Changsha science and technology project kq1801008.
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Li, M., Peng, P., Xu, Y., Xia, H., Qin, Z. (2019). Distributed Landmark Selection for Lower Bound Estimation of Distances in Large Graphs. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_16
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