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 976–1000 of 1570 papers

TitleStatusHype
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages—0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI—0
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection—0
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
Collaborative Interest-aware Graph Learning for Group Identification—0
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance—0
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search—0
Graph Contrastive Learning with Cross-view Reconstruction—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning—0
LLM-enhanced Scene Graph Learning for Household Rearrangement—0
Adversarial Attacks on Deep Graph Matching—0
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees—0
CNN2GNN: How to Bridge CNN with GNN—0
Clustering with Similarity Preserving—0
Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal—0
Time-Series Graph Network for Sea Surface Temperature Prediction—0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
Low-rank Kernel Learning for Graph-based Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified