Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models . We present in detail a method that is especially designed with the requirements of domain experts in mind . Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity . We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation . We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions . A comparison with topics detected by Latent Dirichlet Allocation is also included . Finally, we discuss factors that influence topic interpretation.