In this study, we present a class of directed graphs with bounded degree sequences, which embodies the physical phenomenon of numerosity found in the collective behavior of large animal groups. Behavioral experiments show that an animal's perception of number is capped by a critical limit, above which an individual perceives a nonspecific "many". This species-dependent limit plays a pivotal role in the decision making process of large groups, such as fish schools and bird flocks. Here, we consider directed graphs whose edges model information-sharing between individual vertices. We incorporate the numerosity phenomenon as a critical limit on the intake of information by bounding the degree sequence and include the variability of cognitive processes by using a random variable in the network construction. We analytically compute measures of the expected structure of this class of graphs based on cycles, clustering, and sorting among vertices. Theoretical results are verified with numerical simulation.