Adjustable diffusion matrix-based spectral clustering (ADMSC):
Spectral clustering for protein-protein interaction networks
We propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named the adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks.
ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partition PPI networks into biologically significant clusters with almost equal sizes, while it is very fast, robust and appealing simple.
Reference
Kentaro Inoue, Weijiang Li and Hiroyuki Kurata, Diffusion Model Based Spectral Clustering For Protein-Protein Interaction Networks. PLOS ONE, 5:e12623, 2010
