Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model

TL;DR

This paper introduces an improved implementation of a popular densest subgraph discovery algorithm and a new parallel algorithm that outperforms existing 2-approximation methods, enhancing efficiency in graph data analysis.

Contribution

It presents a novel parallel algorithm for densest subgraph discovery that surpasses the performance of traditional 2-approximation algorithms.

Findings

Improved implementation of existing densest subgraph algorithm

A new parallel algorithm with better results than 2-approximation

Enhanced efficiency in shared memory models

Abstract

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.