Relational datasets are often modeled with graphs and hypergraphs: objects become vertices and relations become edges or hyperedges. Algorithms for graph and hypergraph visualization aim to present such data in an effective and aesthetically appealing way. From a theoretical point of view, the underlying problems give rise to classical algorithmic and complexity questions. From a practical point of view, building functional and usable visualization systems is associated with questions about scalability and usability. We will discuss our general visualization framework of utilizing familiar data representation metaphors, such as geographic maps and metro maps. While graphs and hypergraphs often require considerable effort to comprehend, such representations are more intuitive, as most people are already proficient with such maps and standard interactions via zooming and panning.