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 251–275 of 1570 papers

TitleStatusHype
Online Graph Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation—0
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning—0
Network Games Induced Prior for Graph Topology Learning—0
Graph Learning for Numeric PlanningCode1
RAGraph: A General Retrieval-Augmented Graph Learning FrameworkCode1
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial—0
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting—0
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized PreferenceCode1
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to AdaptationCode2
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Benchmarking Graph Learning for Drug-Drug Interaction Prediction—0
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete DataCode0
Homomorphism Counts as Structural Encodings for Graph LearningCode0
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive DiagnosisCode1
Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Learning Graph Quantized TokenizersCode1
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models—0
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors—0
Online Multi-modal Root Cause Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified