SEQUOIA: Significance enhanced network querying through context-sensitive random walk and minimization of network conductance

We propose a novel network querying algorithm, which adopts the context-sensitive random walk (CSRW) model and the concept of the network conductance. The proposed algorithm identifies the seed network in the target network based on the CSRW node correspondence scores. Then, the seed network is extended by adding the nodes that can minimize the network conductance until the extended network meets the stop conditions. Finally, in order to enhance the biological significance of the querying result, less-relevant nodes are removed based on the extension reward score. Performance assessment based on real protein-protein-interaction (PPI) networks and known biological complexes shows that proposed algorithm outperforms other state-of-the-art network querying algorithms and enhances the biological significance of the querying results.