Abstract
Graph sampling is an important technology for the network visualization. In this paper, we use the normalized Laplacian spectrum to evaluate diverse biased sampling algorithms on Internet topologies, and numerically find that the simple random walk (SRW) sampling performs much better than other sampling algorithms (e.g., breadth first search, forest fire and random jump). Moreover, we analyze the deficiency of the SRW using the physical meaning of the normalized Laplacian spectrum on the size-independent Internet structure. Finally, we indicate that more multi-homed nodes should be added and more inner binate nodes should be reduced for better performance of the SRW sampling graphs on the normalized Laplacian spectrum which is a powerful tool for the study of size-independent structure embedded in evolving systems.
B. Jiao–The research field of Dr. Bo Jiao includes evolving network and spectral graph theory.
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Acknowledgments
We would like to thank the anonymous reviewers for their comments that helped improve this paper. This paper is supported by the National Natural Science Foundation of China with Grant Nos. 61402485 and 61303061.
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Jiao, B., Guo, R., Jin, Y., Yuan, X., Han, Z., Huang, F. (2017). An Improvement Direction for the Simple Random Walk Sampling: Adding Multi-homed Nodes and Reducing Inner Binate Nodes. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_64
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DOI: https://doi.org/10.1007/978-3-319-59288-6_64
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