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 1176–1200 of 1570 papers

TitleStatusHype
Effective and Efficient Graph Learning for Multi-view Clustering—0
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution—0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
Efficient and Stable Graph Scattering Transforms via Pruning—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks—0
Efficient Tree-based Approximation for Entailment Graph Learning—0
EG-Gaussian: Epipolar Geometry and Graph Network Enhanced 3D Gaussian Splatting—0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
A Metric for the Balance of Information in Graph Learning—0
_2-norm Flow Diffusion in Near-Linear Time—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures—0
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial—0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Analysis of Total Variation Minimization for Clustered Federated Learning—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified