Clustered graphs are a versatile representation formalism for expressing relations between entities, and simultaneously, reflecting their hierarchical structure. This makes clustered graphs well-suited to model complex structured data. However, obtaining appealing drawings of clustered graphs is a challenging task. We employ the landscape metaphor to visualize clustered graphs in a cheminformatics application. In order to browse chemical compound libraries in a systematic way, we consider two different molecular similarity concepts. Combining the scaffold-based cluster hierarchy with molecular similarity graphs allows for new insights in the analysis of large molecule libraries. Here, like in certain other application domains, the cluster hierarchy does not necessarily reflect the underlying graph structure. We improve the approach taken in [1] by applying a modified treemap algorithm for node positioning that takes the edges of the graph into account. Experiments with real-world instances clearly show that the new algorithm leads to significant improvements in terms of the edge lengths.

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