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.
By Fanlong Zeng, Wensheng Gan, Kangjie Chen, Philip S. Yu
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.
By Li Yuan, Yaxin Hou, Jiawei Tang, Yongbiao Gao, Yuheng Jia
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.
By Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Jun Hu, Jia Chen
arXiv:2607. 03770v1 Announce Type: cross Abstract: Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance.
By Hyunsoo Kim, Jungmyung Wi, Soobin Um, Donghyun Kim, Suhyun Kim
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.
By Mohammed A. Ramadhan, Abdulhakeem O. Mohammed
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.
By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer
arXiv:2606. 28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks.
By Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi
The paper introduces KAIROS, a self‑supervised framework that embeds a differentiable Koopman operator into a dynamic graph contrastive learning loop. By linearizing temporal evolution in the embedding space, KAIROS jointly optimizes raw node features and a graph‑diffused structural view using multi‑granularity contrastive objectives across time windows. For anomaly detection, it combines Koopman prediction residuals with temporal inconsistency and local neighborhood deviation, achieving state‑of‑the‑art results on nine dynamic graph benchmarks and competitive performance on unsupervised node classification.
By Md Abrar Jahin, Taufikur Rahman Fuad, Md Rizwan Parvez
arXiv:2507. 14484v2 Announce Type: replace Abstract: In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks.
By Yule Li, Yifeng Lu, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding
arXiv:2608. 13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.
By Yikai Xu, Zhao Chen, Jian Huang
The paper introduces GenGLAD, a generalist graph-level anomaly detection framework that learns transferable anomaly knowledge across diverse domains using discriminative subgraph extraction. It employs a graph neural network to identify anomaly-discriminative substructures and adapts to new domains at test time by evaluating multi-dimensional discrepancies between subgraphs and contextual information. Experiments on varied benchmarks show GenGLAD outperforms existing methods in performance, generalization, and data efficiency.
By Junjun Pan, Yixin Liu, Yu Zheng, Fuyi Li, Alan Wee-Chung Liew, Shirui Pan
arXiv:2608. 00346v1 Announce Type: new Abstract: Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy.
By Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan, Manar D. Samad