Complex networks that connect hundreds or thousands of nodes together can function properly through the use of nodes interacting, aﬀecting and regulating one another. Genetic networks underlying high-level hormonal changes are also believed to be complex and most of the time are not yet very well understood. There are also diﬀerent networks with common genes that start to activate or deactivate at the same time, which also adds to the diﬃculty of the problem of understanding genetic networks. Therefore, ﬁnding genes transcriptionally active in a gene set that are responsible for a change in the human body is a key point in reaching the underlying network structure. Here, we present a new technique that searches for these nodes in a set of variables that connect to form a network. It is a stepwise greedy search that investigates the change in a chosen network when one node is taken out at a time. Our simulated genetic data results show that our method is successful for diﬀerentiating between transcriptionally active nodes and background nodes with a p-value of less than 0.05. As real biological data, we used rat RNA-seq data taken for initiation-of-puberty research. Our method found new genes as well as conﬁrming previously known genes with signiﬁcant enrichment results taking charge in functions such as transcritional binding, histone modiﬁcations, and reproductive development.