Motif-aware curriculum learning for node classification.

Area of Science:

• Graph Learning
• Machine Learning
• Network Analysis

Background:

• Node classification is vital in graph learning, with Graph Neural Networks (GNNs) being a popular method.
• Conventional GNNs can suffer from accuracy and robustness issues due to uniform treatment of training nodes.
• Existing curriculum learning approaches for GNNs overlook subgraph structural information.

Purpose of the Study:

• To propose a novel approach, Motif-aware Curriculum Learning for Node Classification (MACL), to enhance GNN performance.
• To incorporate subgraph structural information and node quality measurement into the GNN learning process.
• To address the limitations of existing methods by leveraging motif structures for organized learning.

Main Methods:

• Developed a novel Motif-aware Curriculum Learning (MACL) approach for node classification.
• Designed a motif-aware difficulty measurer to assess the complexity of training nodes.
• Implemented a training scheduler to strategically introduce nodes during the GNN training process.

Main Results:

• Extensive experiments were conducted on five diverse datasets.
• The results demonstrate that integrating MACL with GNNs significantly improves node classification accuracy.
• MACL effectively utilizes subgraph information and node quality for a more organized learning process.

Conclusions:

• Motif-aware Curriculum Learning (MACL) offers a promising advancement in GNN-based node classification.
• The method enhances GNNs by incorporating structural insights from graph motifs.
• MACL provides a more robust and accurate approach to node classification by organizing the learning process.