基于共享内存模型的可否认子图发现并行算法
Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model
摘要：
寻找图的稠密成分的问题是数据分析中广泛探索的领域，在社区挖掘，垃圾邮件检测，计算机安全和生物信息学等领域和分支研究中具有不同的应用。该研究项目探索了以前可用的算法，以研究它们并确定可能导致改进版本的潜在修改，从而实现相当大的性能和效率飞跃。此外，努力也转向设计一个新的算法的问题的denominant子图发现。本文提出了一种改进的实现，广泛使用的dennial子图发现算法和一种新的并行算法，产生更好的结果比2-近似。
英文摘要：
The problem of finding dense components of a graph is a widely explored area in data analysis, with diverse applications in fields and branches of study including community mining, spam detection, computer security and bioinformatics. This research project explores previously available algorithms in order to study them and identify potential modifications that could result in an improved version with considerable performance and efficiency leap. Furthermore, efforts were also steered towards devising a novel algorithm for the problem of densest subgraph discovery. This paper presents an improved implementation of a widely used densest subgraph discovery algorithm and a novel parallel algorithm which produces better results than a 2-approximation.
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。
