Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

LAMDA: Aiding Visual Exploration of Atomic Displacements in Molecular Dynamics Simulations.

IEEE transactions on visualization and computer graphics·2026
Same author

Generative Graph Dictionary Learning.

Proceedings of machine learning research·2025
Same author

FEWSim: A Visual Analytic Framework for Exploring the Nexus of Food-Energy-Water Simulations.

IEEE computer graphics and applications·2025
Same author

Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey.

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining·2025
Same author

Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same author

ArieL: Adversarial Graph Contrastive Learning.

ACM transactions on knowledge discovery from data·2025
Same journal

Modeling the transmission dynamics of racism propagation with community resilience.

Computational social networks·2021
Same journal

A model for the co-evolution of dynamic social networks and infectious disease dynamics.

Computational social networks·2021
Same journal

Influence spreading model used to analyse social networks and detect sub-communities.

Computational social networks·2018
Same journal

Network partitioning algorithms as cooperative games.

Computational social networks·2018
Same journal

Social learning for resilient data fusion against data falsification attacks.

Computational social networks·2018
Same journal

Tracking online topics over time: understanding dynamic hashtag communities.

Computational social networks·2018
查看所有相关文章

相关实验视频

Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

564

图形卷积网络:一个全面的审查.

Si Zhang1, Hanghang Tong1, Jiejun Xu2

  • 1University of Illinois Urbana-Champaign, Champaign, USA.

Computational social networks
|November 2, 2023
PubMed
概括
此摘要是机器生成的。

本调查回顾了图形卷积网络 (GCNs),这是图形表示学习的关键深度学习方法. 它根据卷积类型和应用对GCN进行了分类,强调了这个新兴领域的挑战和未来研究方向.

关键词:
聚合机制 聚合机制深度学习是一种深度学习.图表卷积网络的图表卷积网络.图形表示学习学习学习图形表示.空间方法 空间方法频谱方法 频谱方法

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

241
Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

15.0K

相关实验视频

Last Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

564
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

241
Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

15.0K

科学领域:

  • 机器学习 机器学习
  • 图形理论 图形理论
  • 计算机视觉 计算机视觉
  • 生物信息学是一种生物信息学.

背景情况:

  • 图形对于模拟社会网络和生物信息学等不同领域的复杂关系至关重要.
  • 从图形结构数据中学习是具有挑战性的,因为数据异质性和复杂的连接模式.
  • 通过将图形属性映射到低维的欧几里德空间,表示学习提供了一个解决方案.

研究的目的:

  • 提供对图形卷积网络 (GCNs) 的全面审查,这是图形表示学习的突出深度学习方法.
  • 根据其卷积机制和应用领域对现有的GCN模型进行分类.
  • 确定当前的挑战,并建议GCN领域的未来研究方向.

主要方法:

  • 基于两个主要类型的卷积的GCN模型的分类.
  • 在这些类别中详细突出显示特定的GCN模型.
  • 根据其应用领域对GCN的分类.

主要成果:

  • 现有的GCN模型根据卷积技术被分为不同的类别.
  • 介绍了GCN的分类学,按它们的应用领域进行排列.
  • 确定了关键挑战和潜在的未来研究途径.

结论:

  • 图形卷积网络代表了对图形表示的深度学习的重大进展.
  • 通过分类帮助识别研究差距,了解GCN的景观.
  • 需要进一步的研究来应对现有挑战,并提高GCN的能力.