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Published in:
2010 | OriginalPaper | Chapter
Extract Backbones of Large-Scale Networks Using Data Field Theory
Authors : Zhang Shuqing, Li Deyi, Han Yanni, Xing Ru
Published in: Advances in Wireless Networks and Information Systems
Publisher: Springer Berlin Heidelberg
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The last decade has witnessed a thriving development of the World Wide Web and internet networks, people are involved into different communication networks. With the increasing scale of these networks, it has left a challenge to understand their holistic features, to know which members and the relationships among them, that is, backbone, play vital roles. Based on data field theory, this paper proposes an algorithm that offers a practical procedure to extract backbone of large-scale networks. For dealing with the uncertainty of granularities of different backbones, we present a measurement to validate which is the optimal backbone with the most suitable granularity. We apply our method to the identical real-world networks and compare the results with several alternative backbone extracting methods. The experimental results illustrate that this backbone extraction algorithm can get a 0.8 score on precision ratios, and the recall ratios approximate reaches 0.5; the results are superior to the compared methods.