基于结构特征的综合图分类方法

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Saiful Islam , Md. Nahid Hasan , Pitambar Khanra
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引用次数: 0

摘要

图结构数据在各个领域的日益流行增强了人们对图分类任务的兴趣。虽然出现了许多复杂的图学习方法,但它们的复杂性往往阻碍了实际实施。在本文中,我们通过提出一种基于基本图结构属性构建特征向量的方法来解决这一挑战。我们证明,尽管这些特性很简单,但它们足够强大,可以捕获同一类中的图的内在特征。我们使用三种不同的机器学习方法来探索我们方法的有效性,强调我们基于特征的分类如何利用同一类中图的固有结构相似性来实现准确的分类。我们的方法的一个关键优势是它的简单性,这使得它易于访问和适应广泛的应用,包括社会网络分析,生物信息学和网络安全。此外,我们进行了大量的实验来验证我们的方法的性能,表明它不仅显示了具有竞争力的性能,而且在某些情况下超过了更复杂的最先进技术的准确性。我们的研究结果表明,关注图的基本特征可以为图分类提供一个强大而有效的替代方案,为研究和实际应用提供了巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A structural feature-based approach for comprehensive graph classification
The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical applications.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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