SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 751–775 of 1570 papers

TitleStatusHype
A Unified Framework for Optimization-Based Graph Coarsening—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Identifying critical nodes in complex networks by graph representation learning—0
Graph Agreement Models for Semi-Supervised Learning—0
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning—0
GRASPEL: Graph Spectral Learning at Scale—0
GrassNet: State Space Model Meets Graph Neural Network—0
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning—0
Graph2Graph Learning with Conditional Autoregressive Models—0
G-Signatures: Global Graph Propagation With Randomized Signatures—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning—0
GRAND++: Graph Neural Diffusion with A Source Term—0
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation—0
Dual Space Graph Contrastive Learning—0
GUNDAM: Aligning Large Language Models with Graph Understanding—0
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—0
Grale: Designing Networks for Graph Learning—0
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting—0
Spectral Maps for Learning on Subgraphs—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
Hyperbolic Geometry in Computer Vision: A Survey—0
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified