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
Higher-Order Positional Encodings for Graph Representation Learning
Many real-world systems exhibit higher-order interactions among groups of entities that cannot be captured by pairwise relationships alone. Graph Transformers and Graph Neural Networks increasingly rely on positional enc…
Graph Representation LearningGraph LearningLet the Heads Talk: Beyond Diagonal Graph Attention
Sheaf Neural Networks generalize scalar-weighted message passing by replacing scalar edge weights with linear transport maps between local feature spaces. Yet the role of this matrix-valued transport is entangled with th…
Relational ReasoningNode ClassificationGraph LearningPACE: 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 Learning