Clique detection with a given reliability

IF 1.2 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-01-29 DOI:10.1007/s10472-024-09928-8

Dmitry Semenov, Alexander Koldanov, Petr Koldanov, Panos Pardalos

引用次数: 0

Abstract

In this paper we propose a new notion of a clique reliability. The clique reliability is understood as the ratio of the number of statistically significant links in a clique to the number of edges of the clique. This notion relies on a recently proposed original technique for separating inferences about pairwise connections between vertices of a network into significant and admissible ones. In this paper, we propose an extension of this technique to the problem of clique detection. We propose a method of step-by-step construction of a clique with a given reliability. The results of constructing cliques with a given reliability using data on the returns of stocks included in the Dow Jones index are presented.

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具有给定可靠性的小群检测

在本文中，我们提出了一个新的小群可靠性概念。聚类可靠性被理解为聚类中具有统计意义的链接数与聚类边数之比。这一概念依赖于最近提出的一项原创技术，该技术可将网络顶点间成对连接的推断分为重要连接和可接受连接。在本文中，我们提出将这一技术扩展到聚类检测问题中。我们提出了一种逐步构建具有给定可靠性的小群的方法。本文介绍了利用道琼斯指数中的股票收益数据构建具有给定可靠性的聚类的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Mathematics and Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

3.00

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.