评价不同真实网络上的图群检测算法

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
P. Oza, Smita Agrawal, Dhruv Ravaliya, Riya Kakkar
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引用次数: 0

摘要

复杂网络是机器学习和数据挖掘的重要工具。底层信息可以帮助理解系统并揭示新信息。社区是网络中紧密相连的子群体。这个社区可以帮助我们揭示很多信息。社区检测问题是在网络中发现社区的一种方法。由于使用了Python和R语言实现的各种社区检测算法,因此许多研究人员使用了igraph库。这些算法采用不同的方法实现,显示出不同的性能结果。我们对社区检测算法进行了评估,并根据其在不同场景下的性能和各种性能指标对其进行了排名。结果表明,与自旋玻璃和前导特征向量算法相比,多级、Leiden社区检测算法和Walk陷阱算法具有最高的性能。基于这些算法的研究结果可以帮助研究人员根据自己的需求从图库中选择算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating the Igraph Community Detection Algorithms on Different Real Networks
Complex networks are an essential tool in machine learning and data mining. The underlying information can help understand the system and reveal new information. Community is sub-groups in networks that are densely connected. This community can help us reveal a lot of information. The community detection problem is a method to find communities in the network. The igraph library is used by many researchers due to the utilization of various community detection algorithms implemented in both Python and R language. The algorithms are implemented using various methods showing various performance results. We have evaluated the community detection algorithm and ranked it based on its performance in different scenarios and various performance metrics. The results show that the Multi-level, Leiden community detection algorithm, and Walk trap got the highest performance compared to spin glass and leading eigenvector algorithms. The findings based on these algorithms help researchers to choose algorithms from the igraph library according to their requirements.  
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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