[ezpuzzle]

Graph visualizations are more useful for data science than end user tasks.

Even in lower cardinality contexts like music genres users are better served by traversing topics through a similarity mechanism (e.g. list of suggested other topics) as it gets to user benefit sooner. How often are users clicking far away topics from their area of interest? How do they know which direction to travel in?

Sufficiently large graphs are always subject to classification mechanisms of human benefit and cardinality. That means most graphs are best accessed through hierarchical interfaces.

One interesting thing is the weights of classification, e.g a band could play multiple genres but are they more representative of some than others. This might benefit from a creative interface to represent how dimensionally similar a topic is to some ideal entity in each classification value. Possibly a graph?