Multi-Objective Optimization for Relevant Sub-graph Extraction

In recent years, graph clustering methods have rapidly emerged to mine latent knowledge and functions in networks. Most sub-graphs extracting methods that have been introduced fall into graph clustering. In this paper, a novel trend of relevant sub-graphs extraction problem was considered as multi-objective optimization. Genetic Algorithms (GAs) and Simulated Annealing (SA) were then used to solve the problem applied to biological networks. Comparisons between GAs, SA and Markov Cluster Algorithm (MCL) were carried out and the results showed that the proposed approach is superior. A biological case study shows that our method is able to discover additional protein complex members from an incomplete list of experimentally identified proteins and Pro-tein Protein Interaction (PPI) network.