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Performance Analysis of Graph Laplacian Matrices in Detecting Protein Complexes

초록

영어

Detecting protein complexes is an important way to discover the relationship between network topological structure and its functional features in protein-protein interaction (PPI) network. The spectral clustering method is a popular approach. However, how to select its optimal Laplacian matrix is still an open problem. Here, we analyzed the performances of three graph Laplacian matrices (unnormalized symmetric graph Laplacians,, normalized symmetric graph Laplacians and normalized random walk graph Laplacians, respectively) in yeast PPI network. The comparison shows that the performances of unnormalized and normalized symmetric graph Laplacian matrices are similar, and they are better than that of normalized random walk graph Laplacian matrix. It is helpful to choose proper graph Laplacian matrix for PPI networks’ analysis.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Experimental Data
  2.2. Spectral Clustering Method
  2.3. Evaluation Criteria
 3. Results and Discussion
  3.1. Parameter gap
  3.2. Analysis of the Performance of Three Matrices
 4. Conclusions
 Acknowledgements
 References

저자정보

  • Dong Yun-yuan College of Computer, National University of Defense Technology
  • Keith C.C. Chan Department of computing, Hong Kong Polytechnic University
  • Liu Qi-jun College of Science, National University of Defense Technology
  • Wang Zheng-hua College of Computer, National University of Defense Technology

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