统一框架下最密子图发现的深入分析
In-depth Analysis of Densest Subgraph Discovery in a Unified Framework
AI总结：
本文提出统一框架系统比较从小规模到十亿级图上的最密子图发现算法，并结合现有技术提出快至10倍且精度保证相同的新变体，为后续研究提供新见解。
摘要：
作为图挖掘中的一个基础课题，最密子图发现（DSD）已经在广泛的现实应用中得到了应用。文献中已经提出了若干种DSD算法，包括精确算法和近似算法。然而，这些算法尚未在相同的实验设置下进行系统而全面的比较。在本文中，我们首先提出了一个统一框架，从高层次视角将所有DSD算法纳入其中。然后，我们在从小规模到十亿级规模的多种图上，对具有代表性的DSD算法进行了广泛比较，并检验了所有方法的有效性。此外，我们通过结合现有技术，提出了DSD算法的新变体，这些变体在具有相同精度保证的前提下，比最先进的算法快至多10倍。最后，基于这些发现，我们提出了有前景的研究机会。我们相信，对现有算法行为的更深入理解可以为未来研究提供新的有价值的见解。代码已发布在 https://anonymous.4open.science/r/DensestSubgraph-245A。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
