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.