Detecting protein complex based on hierarchical compressing network embedding

Detecting protein complexes from protein-protein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining high-density regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPC-HCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPC-HCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPC-HCNE outperforms those state-of-the-art complex detection methods.