Graph Learning
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Benchmarks
CAMELS
Most implemented
Graph Random Neural Network for Semi-Supervised Learning on Graphs
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
A Fair Comparison of Graph Neural Networks for Graph Classification
Topological Deep Learning: Going Beyond Graph Data
Papers
PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning
Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledge held by other subgraphs may complement a receiver's Local model, but an incompatible transfer can override r…
Graph LearningCone Extended Rayleigh Quotients for Directed Graph Learning: Minimax Spectral Certificates, Sensitivity, and Adaptive Control
Directed graph learning naturally leads to trainable nonsymmetric propagation operators with distinct right and left spectral structures. Building on the two-sided cone Rayleigh framework for generalized pencils \[ B_θ-λ…
Graph LearningRethinking Message Passing as Retrieval for Text-Attributed Graph Learning
Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise multilayer perceptrons (MLPs). Despite its …
Graph LearningWhy Does Graph Learning Fail to Fully Benefit from a Text Teacher?
Graph neural networks (GNNs) are widely used to represent complex interactions and relationships among entities. We investigate a multimodal model that combines two complementary ideas: a self-supervised method that enab…
Graph LearningAre LLM-Enhanced GNNs Privacy-Safe?
Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving rise to LLM-enhanced GNNs that achieve substantial performance gains. H…
Graph LearningOpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning
Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe externally, and relatively resistant to presentation attacks. However, rel…
Graph Learning