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ARXIV:2602.03808 · GRAPH NEURAL NETWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.03808GRAPH NEURAL NETWORKSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance.
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Pain A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance.
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A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance. To solve this problem, we propose a Curriculum-Guided Feature Learning and Three-Stage Attention Network (CL3AN-GNN),…
Imbalanced node classification in graph neural networks (GNNs) happens when some labels are much more common than others, which causes the model to learn unfairly and perform badly on the less common classes. To…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. This foundation enables stable early learning despite label skew.
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 5.0/10.
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A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance.
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10.48550/arXiv.2602.03808A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance.
Abstract
Imbalanced node classification in graph neural networks (GNNs) happens when some labels are much more common than others, which causes the model to learn unfairly and perform badly on the less common classes. To solve this problem, we propose a Curriculum-Guided Feature Learning and Three-Stage Attention Network (CL3AN-GNN), a learning network that uses a three-step attention system (Engage, Enact, Embed) similar to how humans learn. The model begins by engaging with structurally simpler features, defined as (1) local neighbourhood patterns (1-hop), (2) low-degree node attributes, and (3) class-separable node pairs identified via initial graph convolutional networks and graph attention networks (GCN and GAT) embeddings. This foundation enables stable early learning despite label skew. The Enact stage then addresses complicated aspects: (1) connections that require multiple steps, (2) edges that connect different types of nodes, and (3) nodes at the edges of minority classes by using adjustable attention weights. Finally, Embed consolidates these features via iterative message passing and curriculum-aligned loss weighting. We evaluate CL3AN-GNN on eight Open Graph Benchmark datasets spanning social, biological, and citation networks. Experiments show consistent improvements across all datasets in accuracy, F1-score, and AUC over recent state-of-the-art methods. The model's step-by-step method works well with different types of graph datasets, showing quicker results than training everything at once, better performance on new, imbalanced graphs, and clear explanations of each step using gradient stability and attention correlation learning curves. This work provides both a theoretically grounded framework for curriculum learning in GNNs and practical evidence of its effectiveness against imbalances, validated through metrics, convergence speeds, and generalisation tests.
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PROBLEM
A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance. To solve this problem, we propose a Curriculum-Guided Feature Learning and Three-Stage Attention Network (CL3AN-GNN), a learning netw...
METHOD
Imbalanced node classification in graph neural networks (GNNs) happens when some labels are much more common than others, which causes the model to learn unfairly and perform badly on the less common classes. To solve this problem, we propose a Curriculum-Guided Feature Learning...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. This foundation enables stable early learning despite label skew.
WHY NOW
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance. To solve this problem, we propose a Curriculum-Guided Feature Learning and Three-Stage Attention Network (CL3AN-GNN), a learning network that uses a three-step attention system (Engage, Enact, Embed) similar to how humans learn.
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partial
Imbalanced node classification in graph neural networks (GNNs) happens when some labels are much more common than others, which causes the model to learn unfairly and perform badly on the less common classes. To solve this problem, we propose a Curriculum-Guided Feature Learning and Three-Stage Attention Network (CL3AN-GNN), a learning network that uses a three-step attention system (Engage, Enact, Embed) similar to how humans learn.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. This foundation enables stable early learning despite label skew.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A neural network framework that improves imbalanced graph classification using a novel curriculum learning approach for fairness and performance.
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