Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model
This work addresses densest subgraph discovery for applications like community mining and spam detection, but it appears incremental as it builds on prior algorithms.
The researchers tackled the problem of densest subgraph discovery by improving an existing algorithm and developing a novel parallel algorithm that outperforms a 2-approximation, achieving better results in performance and efficiency.
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.