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Network Pharmacology, 2016, 1(1): 15-35
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Article

Generate networks with power-law and exponential-law distributed degrees: with applications in link prediction of tumor pathways

WenJun Zhang1, Xin Li2
1School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; International Academy of Ecology and Environmental Sciences, Hong Kong
2College of Plant Protection, Northwest A & F University, Yangling 712100, China; Yangling Institute of Modern Agricultural Standardization, Yangling 712100, China

Received 13 August 2015;Accepted 20 September 2015;Published online 1 March 2016
IAEES

Abstract
In present study I proposed a method for generating biological networks based on power-law (p(x)=x-a) and exponential-law (p(x)=e-ax) distribution functions. Given the parameter of power-law or exponential-law distribution function, a, the algorithm generates an expected frequency distribution according to the given parameter, thereafter creates an adjacency matrix in which (practical) frequency distribution of node degrees matches the expected frequency distribution. The results showed that power-law distribution function performs much better than exponential-law distribution function in generating networks. Using the revised algorithm, tumor related networks (pathways) are simulated and predicted. The results prove that the algorithm is overall effective in predicting network links (14.6%-21.2%of correctly predicted links against 0.1%-3.4% of that for random assignments). Matlab codes of the algorithms are given also.

Keywords power-law;exponential-law;degree distribution;adjacency matrix;network generation;link prediction.


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