基于 GCN 的弱监督群落检测与更新结构中心选择

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-01-03 DOI:10.1080/09540091.2023.2291995

Liping Deng, Bing Guo, Wen Zheng

引用次数: 0

摘要

社群检测是网络学习中的一个经典问题。半监督网络学习需要一定量的已知样本，而样本标注费时费力。I...

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

GCN-based weakly-supervised community detection with updated structure centres selection

Community detection is a classic problem in network learning. Semi-supervised network learning requires a certain amount of known samples, while sample annotation is time-consuming and laborious. I...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.