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 276–300 of 1570 papers

TitleStatusHype
Evaluating and Improving Graph-based Explanation Methods for Multi-Agent CoordinationCode1
Covariant Compositional Networks For Learning GraphsCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Dynamically Expandable Graph Convolution for Streaming RecommendationCode1
Fast and Distributed Equivariant Graph Neural Networks by Virtual Node LearningCode1
Fast Optimizer BenchmarkCode1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
FedHGN: A Federated Framework for Heterogeneous Graph Neural NetworksCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Data Augmentation for Deep Graph Learning: A SurveyCode1
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed GraphsCode1
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-DesignCode1
Generative 3D Part Assembly via Dynamic Graph LearningCode1
Generative Causal Explanations for Graph Neural NetworksCode1
NCAGC: A Neighborhood Contrast Framework for Attributed Graph ClusteringCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
Continuity Preserving Online CenterLine Graph LearningCode1
Gradient Gating for Deep Multi-Rate Learning on GraphsCode1
GRAND+: Scalable Graph Random Neural NetworksCode1
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph LearningCode1
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified