Rank-based construction guarantees that each vertex is reachable from at least vertices in the graph. As reflected in Table 2, no singletons were created even for the graph with the least number of edges. With this construction, even with 621 edges, we can already get relationships involving all genes in the **data** set. In contrast with the graph obtained by value-based with = 0.90 with 1, 177 edges,  we can only relate about 63% of the genes in the **data** set. However, this construction also allows edges with weak correlation to be part of the network. We show the comparison of the total number of edges between all the graphs obtained by value-based and rank-based construction. In Figure 6, we see that the total number of edges as we increase is far below the total number of edges for the value-based construction. The highest number of edges is with parameter = 10 which is approximately 17% of the total number of edges of the graph with = 0.70. In Figures 7 and 8, we show the two graphs obtained by rankbased construction with parameters = 2 and = 10, respectively. We visualized both graphs using the Fruchterman-Reingold algorithm to compute for the position of the nodes and used the biological classification to color the set of nodes. With = 2, the resulting graph consists of all the 384 genes in the original **data** set. The genes belonging to groups are not visually separated as compared to the graph obtained by = 10. Even though the graph uses way less number of edges as compared to the graph obtained through valuebased with = 0.70. The visual closeness of the nodes belonging to the same functional group is present. The temporal relationship genes that are present in the original NMDS visualization are also reflected. Visually, we can see the proximity of genes belonging to the same functional groups for both value-based and rank-based construction with = 0.70 and = 10, respectively. According to Ruan et al., [11], the comparison of the rank-based and the value-based construction has not been rigorously examined in the literature. In the following section, we will test whether which of the following graphs can be used to predict functional groups using community detection algorithms.