The standard approach for partitioning a **data** set is through clustering. The works of Ruan et al. [11] have proven that for some **data** sets, community detection in graphs provides more accurate partitions. In line with this, we use 4 different community detection algorithms to predict the grouping of genes based on the 5 functional groups. The first three algorithms use the concept of modularity while the last algorithm uses edge-betweenness. We'll discuss these two network metrics in the succeeding subsections. We subjected the largest component of each graph obtained by using value-based and rank-based construction with varying input parameters. Here, we will compare how the different community detection algorithms perform in predicting the 5 functional groups.