Subgraph mining algorithms aim at the detection of dense clusters in a graph. In recent years many graph clustering algorithms have been presented. Most of the algorithms concentrate on undirected or unweighted graphs. In this work, we propose a novel model to determine the interesting subgraphs also for directed and weighted graphs. We use the method of density computation based on influence functions to identify dense regions in the graph. We present different types of interesting subgraphs. In experiments we show the good clustering quality of our GDens algorithm. GDens outperforms competing approaches in terms of quality and runtime.