  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
