  The problem of finding dense components of a graph is a widely explored area
in data analysis, with diverse applications in fields and branches of study
including community mining, spam detection, computer security and
bioinformatics. This research project explores previously available algorithms
in order to study them and identify potential modifications that could result
in an improved version with considerable performance and efficiency leap.
Furthermore, efforts were also steered towards devising a novel algorithm for
the problem of densest subgraph discovery. This paper presents an improved
implementation of a widely used densest subgraph discovery algorithm and a
novel parallel algorithm which produces better results than a 2-approximation.
