With the introduction of microarray technology, fresher computational techniques to differentiate such interaction or relationship between genes expressions can be projected and produces an association network. Nevertheless, most microarray computations usually analyse the appearance of genes that are differentially expressed, yet it is of greater significance to identify how all-inclusive association network structures change between two or more biological environs, approximately normal against diseased cell types. In this article we provide a technique for conducting a differential analysis of networks created from microarray data under matching experimental settings and we believe that with appropriate connectivity scores, this will be a suitable tool in reconnoitering changes in network structures under different biological conditions.