Performance analysis of graph Laplacian matrices in detecting protein complexes

Dong Yun-Yuan, Chun Chung Chan, Liu Qi-Jun, Wang Zheng-Hua

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)347-352
Number of pages6
JournalInternational Journal of Security and its Applications
Issue number2
Publication statusPublished - 8 Aug 2012


  • Graph Laplacian matrix
  • Protein complex
  • Protein-protein interaction network
  • Spectral clustering method

ASJC Scopus subject areas

  • General Computer Science


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