比较图聚类:设置分区度量与图感知度量。

IF 20.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2021-06-01 Epub Date: 2021-05-11 DOI:10.1109/TPAMI.2020.3009862

Valerie Poulin, Francois Theberge

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引用次数: 3

摘要

在本文中，我们提出了一组考虑图的拓扑结构的图划分相似度度量。这些图感知度量是使用非专门为图设计的集分区相似性度量的替代方法。图感知度量和设置分区度量这两种度量在解决问题方面表现出相反的行为，并提供比较图分区所需的补充信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Comparing Graph Clusterings: Set Partition Measures vs. Graph-Aware Measures.

In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graphs. The two types of measures, graph-aware and set partition measures, are shown to have opposite behaviors with respect to resolution issues and provide complementary information necessary to compare graph partitions.

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来源期刊

IEEE Transactions on Pattern Analysis and Machine Intelligence 工程技术-工程：电子与电气

CiteScore

28.40

自引率

3.00%

发文量

885

审稿时长

8.5 months

期刊介绍： The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.