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 451–475 of 1570 papers

TitleStatusHype
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach—0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
CNN2GNN: How to Bridge CNN with GNN—0
Equivariant Polynomials for Graph Neural Networks—0
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters—0
Effective and Efficient Graph Learning for Multi-view Clustering—0
Euclidean geometry meets graph, a geometric deep learning perspective—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning—0
Free Lunch for Privacy Preserving Distributed Graph Learning—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Edge-Featured Graph Attention Network—0
Edge-boosted graph learning for functional brain connectivity analysis—0
ExPath: Towards Explaining Targeted Pathways for Biological Knowledge Bases—0
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning—0
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression—0
Can we Soft Prompt LLMs for Graph Learning Tasks?—0
Explainable and Position-Aware Learning in Digital Pathology—0
FiGLearn: Filter and Graph Learning using Optimal Transport—0
Exploiting Edge Features for Graph Neural Networks—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting—0
Adversarial Attacks on Deep Graph Matching—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified