An improved newman algorithm for mining overlapping modules from protein-protein interaction networks

  • Authors:

  • Xuesong Wang;Lijing Li;Yuhu Cheng

  • Affiliations:

  • School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China

  • Venue:
  • ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
  • Year:
  • 2011

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Abstract

With the development of high-throughput technologies in recent years, more and more scientists focus on protein-protein interaction (PPI) networks. Previous studies showed that there are modular structures in PPI networks. It is well known that Newman algorithm is a classical method for mining associations existed in complex networks, which has advantages of high accuracy and low complexity. Based on the Newman algorithm, we proposed an improved Newman algorithm to mine overlapping modules from PPI networks. Our method mainly consists of two steps. Firstly, we try to discover all candidate nodes whose neighbors belong to more than one module. Secondly, we determine candidate nodes that have positive effects on modularity as overlapping nodes and copy these nodes into their corresponding modules. In addition, owing to the features of existing system noise in PPI networks, we designed corresponding methods for de-noising. Experimental results concerning MIPS dataset show that, the proposed improved Newman algorithm not only has the ability of finding overlapping modular structure but also has low computational complexity.