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 726–750 of 1570 papers

TitleStatusHype
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
A Simple Spectral Failure Mode for Graph Convolutional Networks—0
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
A Benchmark for Fairness-Aware Graph Learning—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
Exploring Edge Disentanglement for Node Classification—0
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning—0
Computing Steiner Trees using Graph Neural Networks—0
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting—0
Exploiting Edge Features in Graph Neural Networks—0
Exploiting Edge Features for Graph Neural Networks—0
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
Explainable and Position-Aware Learning in Digital Pathology—0
Explainability and Graph Learning from Social Interactions—0
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
ExPath: Towards Explaining Targeted Pathways for Biological Knowledge Bases—0
Expanding Semantic Knowledge for Zero-shot Graph Embedding—0
Collaborative Interest-aware Graph Learning for Group Identification—0
A Scalable and Effective Alternative to Graph Transformers—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified