A Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution

Key points

• The learnability of GCNs is intrinsically linked to the neighborhood structure of nodes, making it critical to understand how this structure changes in the presence of label noise.1
• In this work, we investigate how the neighborhood label distribution of a node changes under label noise.1
• We mathematically derive that label noise smooths this distribution, which inherently increases its entropy and severely decreases interclass distinguishability.1
• To address this challenge, we propose Leave-one-out Entropy Node Screening (LENS), an end-to-end framework that identifies noisy nodes by measuring the reduction in neighborhood entropy when a specific node is removed.1

Sources (1)

• [1]A Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 06:22 AM

  Graph Convolutional Networks (GCNs) have demonstrated exceptional performance on graph-structured data.

  The learnability of GCNs is intrinsically linked to the neighborhood structure of nodes, making it critical to understand how this structure changes in the presence of label noise.

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