arXiv Machine Learning
MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification
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arXiv AI
Sep 16
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
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 Machine Learning
Oct 7
Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning
The paper introduces a hub‑spoke latent space design for dual‑mismatched semi‑supervised learning, where known classes are uniformly arranged around a central hub and each class forms a compact cluster around its prototype. The hub serves as an anchor for a low‑evidence region that captures high‑uncertainty unknown class samples, thereby reducing majority‑class domination and improving feature discriminability. Experiments demonstrate that this approach outperforms state‑of‑the‑art methods, achieving up to a 3.25% improvement across various settings.
arXiv AI
Jul 16
NodeImport: Imbalanced Node Classification with Node Importance Assessment
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 AI
Jul 7
Self-Improving Diffusion Classifiers with Minority Preference Optimization
arXiv:2607. 03770v1 Announce Type: cross Abstract: Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance.
arXiv AI
Jun 2
A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
arXiv:2507. 19702v1 Announce Type: cross Abstract: Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains.
arXiv Machine Learning
Jul 31
ARES: Anomaly Recognition Model For Edge Streams
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.