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

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 summarize 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, providing a thorough analysis of DSD algorithms. As a byproduct of our experimental analysis, we are also able to identify new variants of the DSD algorithms over undirected graphs, by combining existing techniques, which are up to 10× 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.