Scholarlink

<p>Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing structured data represented as graphs. They offer significant contributions across various domains due to their ability to effectively capture and process complex relational information. However, most existing GNNs still suffer from undesirable phenomena such as non-robustness, overfitting, and over-smoothing. These challenges have raised significant interest among researchers. In this context, this work aims to address these issues by proposing a new vision of Dropout named A-DropEdge. First, it applies a message-passing layer to ensure the connection between nodes and avoid dropping in the input. Then, the information propagates through many branches with different random configurations to enhance the aggregation process. Moreover, consistency regularization is adopted to perform self-supervised learning. The experimental results on three graph data sets including Cora, Citeseer, and PubMed show the robustness and performance of the proposed approach in mitigating the over-smoothing problem.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Novel dropout approach for mitigating over-smoothing in graph neural networks

  • 期刊论文

  • 发表日期：

    2025年1月21日

查看全文
  • El houssaine Hssayni,
  • Ali Boufssasse,
  • Nour-Eddine Joudar,
  • Mohamed Ettaouil

摘要

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing structured data represented as graphs. They offer significant contributions across various domains due to their ability to effectively capture and process complex relational information. However, most existing GNNs still suffer from undesirable phenomena such as non-robustness, overfitting, and over-smoothing. These challenges have raised significant interest among researchers. In this context, this work aims to address these issues by proposing a new vision of Dropout named A-DropEdge. First, it applies a message-passing layer to ensure the connection between nodes and avoid dropping in the input. Then, the information propagates through many branches with different random configurations to enhance the aggregation process. Moreover, consistency regularization is adopted to perform self-supervised learning. The experimental results on three graph data sets including Cora, Citeseer, and PubMed show the robustness and performance of the proposed approach in mitigating the over-smoothing problem.