Poster "heterophilic graphs" Papers
12 papers found
Conference
A Signed Graph Approach to Understanding and Mitigating Oversmoothing
Jiaqi Wang, Xinyi Wu, James Cheng et al.
NEURIPS 2025arXiv:2502.11394
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Yuhan Chen, Yihong Luo, Yifan Song et al.
ICLR 2025arXiv:2502.17912
6
citations
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
Li Sun, Zhenhao Huang, Ming Zhang et al.
NEURIPS 2025arXiv:2510.17457
1
citations
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
Ming Gu, Zhuonan Zheng, Sheng Zhou et al.
NEURIPS 2025arXiv:2412.09805
3
citations
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
Carlo Abate, Filippo Maria Bianchi
ICLR 2025arXiv:2409.05100
3
citations
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
Shuo Wang, Bokui Wang, Zhixiang Shen et al.
ICML 2025arXiv:2502.02017
19
citations
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang, Jitao Zhao, Dongxiao He et al.
NEURIPS 2025arXiv:2509.22416
3
citations
Spectro-Riemannian Graph Neural Networks
Karish Grover, Haiyang Yu, Xiang song et al.
ICLR 2025arXiv:2502.00401
2
citations
Graph Adversarial Diffusion Convolution
Songtao Liu, Jinghui Chen, Tianfan Fu et al.
ICML 2024arXiv:2406.02059
2
citations
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
ICML 2024arXiv:2310.05105
2
citations
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
MoonJeong Park, Jaeseung Heo, Dongwoo Kim
ICML 2024arXiv:2403.10543
6
citations
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Langzhang Liang, Sunwoo Kim, Kijung Shin et al.
ICML 2024arXiv:2405.20652
13
citations