Abstract
A new method and corresponding numerical procedure are introduced to estimate scaling exponents of power-law degree distribution and hierarchical clustering function for complex networks. This method can overcome the biased and inaccurate faults of graphical linear fitting methods commonly used in current network research. Furthermore, it is verified to have higher goodness-of-fit than graphical methods by comparing the KS (Kolmogorov-Smirnov) test statistics for 10 CNN (Connecting Nearest-Neighbor) networks.
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Communicated by LIU Zeng-rong
Project supported by the National Natural Science Foundation of China (Nos. 70431002, 70401019)
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Yang, B., Duan, Wq. & Chen, Z. New method to estimate scaling exponents of power-law degree distribution and hierarchical clustering function for complex networks. Appl Math Mech 27, 1475–1479 (2006). https://doi.org/10.1007/s10483-006-1104-1
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DOI: https://doi.org/10.1007/s10483-006-1104-1