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.
By Rui Zhao, Bin Shi, Zhiming Liang, Jianfei Ruan, Kai Sun, Bo Dong, Lu Lin
arXiv:2601. 17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics.
By Wei Ju, Wei Zhang, Siyu Yi, Zhengyang Mao, Yifan Wang, Jingyang Yuan, Zhiping Xiao, Ziyue Qiao, Ming Zhang
Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise proposes PCC+GCN, a hybrid framework that refines labels using Particle Competition and Cooperation before training a GCN. PCC identifies suspicious nodes via particle domination dynamics and decides whether to keep, remove, or reassign their labels, optionally augmenting the graph with k‑nearest‑neighbor edges. Evaluated on ten NoisyGL datasets under various noise types, PCC+GCN achieved the highest average accuracy and rank, outperforming baseline GCN by 1.67 percentage points and proving computationally efficient, especially under instance‑dependent noise.
By Fabricio Breve
The paper introduces DeGLIF, a denoising method for graph data that leverages a leave‑one‑out influence function to make node‑level predictions robust to label noise. DeGLIF uses a small clean subset and a theoretically motivated relabelling function to identify and correct noisy nodes without requiring knowledge of the noise model or level. Two variants of DeGLIF are proposed, one of which is proven to increase risk for detected noisy points, and experiments demonstrate that DeGLIF outperforms baseline algorithms on several datasets.
By Pintu Kumar, Nandyala Hemachandra
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
SCARCE is a label‑free representation‑learning framework that improves graph‑level out‑of‑distribution generalization. It uses an environment partition of training graphs—derived from metadata or self‑supervised clustering—to warm‑start a variational graph autoencoder and then applies an adversarial confusion mechanism that prevents a discriminator from distinguishing environments while preserving reconstruction fidelity. Theoretical analysis shows that this environment confusion contracts environment‑discriminative directions, effectively reweighting features to reduce reliance on spurious correlations, and experiments on synthetic and DrugOOD datasets demonstrate state‑of‑the‑art OOD performance among label‑free methods.
By Tianjun Yao, Chen Xi, Xinyang Li, Lixian Zhong
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...
arXiv:2606. 07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis.
By Takuto Takahashi, Itsuki Nakayama, Takahiro Mitani, Ryosuke Kikuchi, Yuya Sasaki, Makoto Onizuka
arXiv:2608. 04377v1 Announce Type: cross Abstract: Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships.
By Mengyao Zhou, Zhiheng Zhou, Xiao Han, Guiying Yan
arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.
By Chen-Hsuan Fang, Wei-Hsinag Chen, Pin-Hsuan Yu, Jung-Hua Wang, Tsung-Wei Pan
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
By Abdullah Shaik, Anwar Said
arXiv:2607. 00671v1 Announce Type: cross Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role.
By Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang, Rizal Fathony, Jia Chen, Bingsheng He