"graph neural networks" Papers
189 papers found • Page 3 of 4
From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks
Amirhossein Farzam, Allen Tannenbaum, Guillermo Sapiro
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Yucheng Wang, Yuecong Xu, Jianfei Yang et al.
GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking
Shu Yin, Peican Zhu, Lianwei Wu et al.
Generalization Error of Graph Neural Networks in the Mean-field Regime
Gholamali Aminian, Yixuan He, Gesine Reinert et al.
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni et al.
GNNs Also Deserve Editing, and They Need It More Than Once
Shaochen (Henry) Zhong, Duy Le, Zirui Liu et al.
GOODAT: Towards Test-Time Graph Out-of-Distribution Detection
Luzhi Wang, Di Jin, He Zhang et al.
Graph2Tac: Online Representation Learning of Formal Math Concepts
Lasse Blaauwbroek, Mirek Olšák, Jason Rute et al.
Graph As Point Set
Xiyuan Wang, Pan Li, Muhan Zhang
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi
Graph Context Transformation Learning for Progressive Correspondence Pruning
Junwen Guo, Guobao Xiao, Shiping Wang et al.
Graph Contrastive Invariant Learning from the Causal Perspective
9672 Yanhu Mo, Xiao Wang, Shaohua Fan et al.
Graph Distillation with Eigenbasis Matching
Yang Liu, Deyu Bo, Chuan Shi
Graph External Attention Enhanced Transformer
Jianqing Liang, Min Chen, Jiye Liang
Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization
Tianrui Jia, Haoyang Li, Cheng Yang et al.
Graph Mixup on Approximate Gromov–Wasserstein Geodesics
Zhichen Zeng, Ruizhong Qiu, Zhe Xu et al.
Graph Neural Network Causal Explanation via Neural Causal Models
Arman Behnam, Binghui Wang
Graph Neural Network Explanations are Fragile
Jiate Li, Meng Pang, Yun Dong et al.
Graph Neural Networks Use Graphs When They Shouldn't
Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach et al.
Graph Neural Networks with a Distribution of Parametrized Graphs
See Hian Lee, Feng Ji, Kelin Xia et al.
Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
Masanobu Horie, NAOTO MITSUME
Graph Neural Prompting with Large Language Models
Yijun Tian, Huan Song, Zichen Wang et al.
Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
Xixun Lin, Wenxiao Zhang, Fengzhao Shi et al.
Graph Positional and Structural Encoder
Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné et al.
HGPrompt: Bridging Homogeneous and Heterogeneous Graphs for Few-Shot Prompt Learning
Xingtong Yu, Yuan Fang, Zemin Liu et al.
Higher-Order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Yiming Huang, Yujie Zeng, Qiang Wu et al.
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, Ismail Ceylan et al.
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
Hypergraph-enhanced Dual Semi-supervised Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi et al.
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
Weijia Zhang, Chenlong Yin, Hao Liu et al.
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
Learning Graph Representation via Graph Entropy Maximization
Ziheng Sun, Xudong Wang, Chris Ding et al.
Learning to Approximate Adaptive Kernel Convolution on Graphs
Jaeyoon Sim, Sooyeon Jeon, InJun Choi et al.
LLaGA: Large Language and Graph Assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal et al.
Long Range Propagation on Continuous-Time Dynamic Graphs
Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio et al.
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
Alexandre Hayderi, Amin Saberi, Ellen Vitercik et al.
MGNet: Learning Correspondences via Multiple Graphs
Dai Luanyuan, Xiaoyu Du, Hanwang Zhang et al.
Mitigating Label Noise on Graphs via Topological Sample Selection
Yuhao Wu, Jiangchao Yao, Xiaobo Xia et al.
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
MoonJeong Park, Jaeseung Heo, Dongwoo Kim
Modelling Microbial Communities with Graph Neural Networks
Albane Ruaud, Cansu Sancaktar, Marco Bagatella et al.
Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing
Hongbin Pei, Yu Li, Huiqi Deng et al.
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Yuchen Zhang, Tianle Zhang, Kai Wang et al.
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
Arjun Subramonian, Levent Sagun, Yizhou Sun
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
Artur Toshev, Jonas Erbesdobler, Nikolaus Adams et al.
Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks
Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
NodeMixup: Tackling Under-Reaching for Graph Neural Networks
Weigang Lu, Ziyu Guan, Wei Zhao et al.
No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
Nimesh Agrawal, Anuj Sirohi, Sandeep Kumar et al.
On dimensionality of feature vectors in MPNNs
César Bravo, Alexander Kozachinskiy, Cristobal Rojas
On the Expressive Power of Spectral Invariant Graph Neural Networks
Bohang Zhang, Lingxiao Zhao, Haggai Maron
On the Generalization of Equivariant Graph Neural Networks
Rafał Karczewski, Amauri Souza, Vikas Garg