In-depth Analysis of Densest Subgraph Discovery in a Unified Framework

TL;DR

This paper presents a unified framework for Densest Subgraph Discovery algorithms, compares them extensively across various graph sizes, introduces faster variants, and discusses future research directions.

Contribution

It provides the first comprehensive comparison of DSD algorithms under the same settings and proposes new, faster variants with maintained accuracy.

Findings

New variants are up to 10X faster than existing algorithms.

Unified framework enables systematic comparison of DSD methods.

Insights into algorithm behavior suggest promising future research directions.

Abstract

As a fundamental topic in graph mining, Densest Subgraph Discovery (DSD) has found a wide spectrum of real applications. Several DSD algorithms, including exact and approximation algorithms, have been proposed in the literature. However, these algorithms have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first propose a unified framework to incorporate all DSD algorithms from a high-level perspective. We then extensively compare representative DSD algorithms over a range of graphs -- from small to billion-scale -- and examine the effectiveness of all methods. Moreover, we suggest new variants of the DSD algorithms by combining the existing techniques, which are up to 10 X faster than the state-of-the-art algorithm with the same accuracy guarantee. Finally, based on the findings, we offer promising research opportunities. We believe that a deeper understanding of the behavior of existing algorithms can provide new valuable insights for future research. The codes are released at https://anonymous.4open.science/r/DensestSubgraph-245A