GraphIFE addresses the class imbalance problem in graph-structured data by tackling a quality inconsistency issue in synthesized nodes. The framework uses graph invariant learning to strengthen embedding space representations and identify invariant features, leading to improved performance on minority classes. Experiments show that GraphIFE consistently outperforms various baselines across multiple datasets.
arXiv:2607. 13837v1 Announce Type: cross Abstract: In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance.
arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.
The paper introduces LEGNN, a lower‑complexity Graph Neural Network designed to handle label noise in node classification. LEGNN reframes the problem as a label ensemble task, using random neighbor masks to gather high‑probability label sets and a partial‑label learning strategy to aggregate them, while also collecting low‑probability labels to mitigate noise. Experiments on six datasets show that LEGNN achieves strong classification performance, computational efficiency, and scalability to graphs with over a hundred thousand nodes and one million edges.
arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard...
The paper introduces LENS, a lightweight algorithm that detects noisy labels in graph data by measuring how removing a node reduces neighborhood entropy. It shows that label noise increases entropy and blurs class boundaries, and proposes a relabeling step to preserve useful features. Experiments demonstrate that LENS outperforms existing noise‑robust methods in accuracy while being an order of magnitude faster.
arXiv:2608. 02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems.
arXiv:2609.36302v1 Announce Type: new Abstract: While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learnin...
arXiv:2606. 17667v1 Announce Type: cross Abstract: In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM).
Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically...
The paper reinterprets graph neural networks (GNNs) as retrieval-augmented models, where each layer uses an MLP on a node representation and a permutation‑invariant summary of retrieved graph context instead of traditional message passing. It introduces RTA, a lightweight MLP‑based framework that replaces structural message passing with label‑aware retrieval and propagation, and provides theoretical links to softmax‑attention message passing and robustness to mis‑retrieved outliers. Experiments on text‑attributed graph benchmarks demonstrate that RTA matches or surpasses strong GNN and graph LLM baselines while improving efficiency and robustness.
Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services.
arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.