Identifying the Communities in the Metabolic Network Using 'Component' Definition and Girvan-Newman Algorithm

Modularization on the metabolic network can help to determine the relationship between community in network and network stability and evolutionary process. In this paper, we selected seven kinds of thermophiles and 7 kinds of mesophiles as the research objects, and constructed their metabolic networks using Pajek algorithm. Next, "component" definition and Girvan-Newman algorithm are used to identify the communities in the metabolic networks. The results showed that ratios of module number to node number are 15.71% and 16.90% respectively in thermophiles metabolic networks, while ratios of module number to node number are 17.61% and 19.79% respectively in mesophiles metabolic networks. The effects of these two methods of modularization show that modular degree in thermophiles is higher than in mesophiles. The minimum of Q function is 0.88, which means the performance of Girvan-Newman algorithm is better to identify communities. In addition, from the number of nodes in communities, we can deduce that the density in thermophilic bacteria metabolic network is larger than in mesophilic bacteria metabolic network.