Sub-optimal Recall in Visual Cluster Retrieval: When Clusters Look Like Bridges

Force-directed node placement algorithms, a popular technique to visualise networks, are known to optimize “cluster separability”: when sets of densely connected nodes get represented as well-separated groups of dots. Using these techniques leads us to conceive networks as sets of clusters connected by bridges. This is also how we tend to think of the “community structure” model embedded in clustering techniques like modularity maximization. Yet this mental model has flaws. We specifically address the notion that clusters (“communities”) necessarily look like groups of dots, through the mediation of a node placement algorithm. Although often true, we provide a reproducible counterexample: topological clusters that look like bridges. First, we present an empirical case that we encountered in a real world situation, while mapping the academic landscape of AI and algorithms. Second, we show how to generate a network of arbitrary size where a cluster looks like a bridge. In conclusion, we open a discussion about layout algorithms as a visual mediation of a network’s community structure. We contend that when it comes to the accuracy of retrieving clusters visually, node placement algorithms have an imperfect recall despite an excellent precision.