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 351–375 of 1570 papers

TitleStatusHype
Structured Landmark Detection via Topology-Adapting Deep Graph LearningCode1
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional NetworksCode1
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge TransferCode1
The general theory of permutation equivarant neural networks and higher order graph variational encodersCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Universal Function Approximation on GraphsCode1
Automating Botnet Detection with Graph Neural NetworksCode1
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning ModelsCode1
Graph Neural Distance Metric Learning with Graph-BertCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
Diffusion Improves Graph LearningCode1
Uncertainty-based graph convolutional networks for organ segmentation refinementCode1
RGB-T Image Saliency Detection via Collaborative Graph LearningCode1
Covariant Compositional Networks For Learning GraphsCode1
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation—0
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Federated Learning with Graph-Based Aggregation for Traffic Forecasting—0
Graph Learning—0
S2FGL: Spatial Spectral Federated Graph LearningCode0
Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based AugmentationsCode0
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning—0
Higher-Order Graph DatabasesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified