Biological network analysis reveals useful hidden information in a very short time span compared to wet lab experiments. In this project, diabetes disease network will be analyzed with the help of mathematical, computational and network based tools. Type II diabetes is becoming major health concern worldwide. There are many factor associated with this disease like developmental, genetic, epigenetic, lifestyle, obesity etc. though it is a metabolic disorder disease caused by mainly insulin resistance and to some extent impaired insulin production. Different network parameters like network density, diameter, degree distribution, network heterogeneity, shortest pathlength etc. and topology will be analyzed for better understanding of network structural property. Eventually structural property will help in understanding network functionality as function is always affected by structure. Candidate genes, proteins or molecules will be identified using different centrality measures like, Degree centrality, Betweenness centrality, Closeness centrality, Eccentricity centrality, Eigenvector centrality. Genes, proteins or molecules will be ranked based on these centrality values. Pagerank algorithm also will be used in ranking. These top ranked gene, protein or molecule will be tested for drugability. Motifs are also known as subgraphs and will be used to identify the structural design principles of the network. If there is any change in genetic pattern, it can be identified by aligning diabetic network with non diabetic network. It will help in understanding the emergent of diabetic network.