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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 1570 papers

TitleStatusHype
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
Equivariant Polynomials for Graph Neural Networks—0
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters—0
Euclidean geometry meets graph, a geometric deep learning perspective—0
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
Expanding Semantic Knowledge for Zero-shot Graph Embedding—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
Explainability and Graph Learning from Social Interactions—0
Explainable and Position-Aware Learning in Digital Pathology—0
Exploiting Edge Features for Graph Neural Networks—0
Exploiting Edge Features in Graph Neural Networks—0
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting—0
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning—0
Exploring Edge Disentanglement for Node Classification—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Higher Order Structures For Graph Explanations—0
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Exponential Family Graph Embeddings—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified