Abstract
A Mechanism-Inferring method of networks exploited from machine learning theory can effectively evaluate the predicting performance of a network model. The existing method for inferring network mechanisms based on a census of subgraph numbers has some drawbacks, especially the need for a runtime increasing strongly with network size and network density. In this paper, an improved method has been proposed by introducing a census algorithm of subgraph concentrations. Network mechanism can be quickly inferred by the new method even though the network has large scale and high density. Therefore, the application perspective of mechanism-inferring method has been extended into the wider fields of large-scale complex networks. By applying the new method to a case of protein interaction network, the authors obtain the same inferring result as the existing method, which approves the effectiveness of the method.
Similar content being viewed by others
References
M. E. J. Newman, The structure and function of complex networks, SIAM Review, 2003, 45(2): 167–256.
R. Albert and A. L. Barabási, Statistical mechanics of complex networks, Rev. Mod. Phys., 2002, 74(1): 47–97.
D. J. Watts and S. H. Strogatz, Collective dynamics of small-world networks, Nature, 1998, 393(6684): 440–442.
A. L. Barabási and R. Albert, Emergence of scaling in random networks, Science, 1999, 286(10): 509–512.
M. Middendorf, E. Ziv, and C. H. Wiggins, Inferring network mechanisms: The Drosophila melanogaster protein interaction network, PNAS USA, 2005, 102(9): 3192–3197.
Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, J. of Computer and System Sciences, 1997, 55(1): 119–139.
A. Vázquez, R. Dobrin, D. Sergi, J. P. Eckmann, Z. N. Oltvai, and A. L. Barabási, The topological relationship between the large-scale attributes and local interaction patterns of complex networks, PNAS USA, 2004, 101(52): 17940–17945.
W. K. Xiao, J. Ren, F. Qi, Z. W. Song, M. X. Zhu, H. F. Yang, H. Y. Jin, B. H. Wang, and T. Zhou, Empirical study on clique-degree distribution of networks, Phys. Rev. E, 2007, 76: 1–4.
N. Pržulj, Biological network comparison using graphlet degree distribution, Bioinformatics, 2006, 23(2): 177–183.
A. Vázquez, J. G. Oliveira, and A. L. Barabási, Inhomogeneous evolution of subgraphs and cycles in complex networks, Phys. Rev. E, 2005, 71: 1–4.
N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon, Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs, Bioinformatics, 2004, 20(11): 1746–1758.
S. Wernicke, A faster algorithm for detecting network motifs, Lecture Notes in Bioinformatics, 2005, 3692: 165–177.
N. Alon, R. Yuster, and U. Zwick, Finding and counting given length cycles, Algorithmica, 1997, 17(3): 209–223.
J. Nesetril and S. Poljak, On the complexity of the subgraph problem, Commen. Math. Univ. Carol., 1985, 26(2): 415–419.
R. A. Duke, H. Lefmann, and V. Rodl, A fast approximation algorithm for computing the frequencies of subgraphs in a given graph, SIAM J. on Computing, 1995, 24(3): 598–620.
Author information
Authors and Affiliations
Corresponding author
Additional information
This research was supported by the National Natural Science Foundation of China under Grant No. 70401019.
Rights and permissions
About this article
Cite this article
Yang, B., Chen, X. Method for quickly inferring the mechanisms of large-scale complex networks based on the census of subgraph concentrations. J Syst Sci Complex 22, 252–259 (2009). https://doi.org/10.1007/s11424-009-9161-y
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11424-009-9161-y