一种有效的可否认子图发现算法
Efficient Algorithms for Densest Subgraph Discovery
摘要：
属性子图发现（DSD）是图挖掘中的一个基本问题。它已经被研究了几十年，并广泛应用于各个领域，包括网络科学，生物分析和图形数据库。给定一个图G，DSD的目标是找到G的具有最高密度的子图D（例如，D中的边数除以顶点数）。由于DSD是很难解决的，我们提出了一个新的解决方案的范例。我们的主要观察是，可以通过k-核（G的一种稠密子图）精确地找到一个稠密子图，并有理论保证。基于这种直觉，我们开发了有效的精确和近似的解决方案DSD。此外，我们的解决方案能够找到广泛的图密度定义，包括基于密度和一般模式为基础的密度的dennial子图。我们对11个真实数据集进行了广泛的实验评估。我们的研究结果表明，我们的算法比现有的方法快四个数量级。
英文摘要：
Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G, DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that a densest subgraph can be accurately found through a k-core (a kind of dense subgraph of G), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based and general pattern-based density. We have performed extensive experimental evaluation on eleven real datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.
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。
