There have been many studies on network visualization efficacy. This work expands on previous studies examining the efficacies of the common visualizations of Node-Link and Adjacency Matrix visualizations. We add a third visualization, Bipartite Layout, and examine the efficacy of this visualization compared to those examined by previous studies. Our study was performed on the crowdsourcing platform, Mechanical Turk, on a total of 72 participants with varying experiences with network visualization. Overall, we were able to largely confirm the results of previous visualization efficacy studies. We found that the Bipartite Layout mostly shares task-specific efficacy with the Adjacency Matrix visualization. We also found that the effect of increasing network size on task completion accuracy on the Bipartite Layout appears to be similar to that of the Adjacency Matrix. Similarly, the effect due to increasing network density is comparable to that of the Node-Link visualization. We found that Bipartite Layouts tend to perform better than other visualizations on large, sparse graphs. We also find that interactivity has a significant effect on network visualization efficacy. Further study will be needed to see which interactive elements cause which effects, and to refine further our understanding of the efficacies of Bipartite Layout.