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 1301–1350 of 1570 papers

TitleStatusHype
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
Architectural Implications of Embedding Dimension during GCN on CPU and GPU—0
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
Accurately Solving Rod Dynamics with Graph Learning—0
A Primer on Temporal Graph Learning—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Self-supervised Graph Neural Network for Mechanical CAD Retrieval—0
Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection—0
Self-supervised Learning: Generative or Contrastive—0
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment—0
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation—0
A novel hybrid time-varying graph neural network for traffic flow forecasting—0
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction—0
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics—0
Semi-decentralized Federated Ego Graph Learning for Recommendation—0
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction—0
Semi-supervised Data Representation via Affinity Graph Learning—0
Unfolded Deep Graph Learning for Networked Over-the-Air Computation—0
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network—0
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation—0
Semi-Supervised Hierarchical Graph Classification—0
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems—0
Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data—0
Series Photo Selection via Multi-view Graph Learning—0
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements—0
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search—0
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation—0
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning—0
SGL: Spectral Graph Learning from Measurements—0
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters—0
Equivariant Polynomials for Graph Neural Networks—0
Euclidean geometry meets graph, a geometric deep learning perspective—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Expanding Semantic Knowledge for Zero-shot Graph Embedding—0
ExPath: Towards Explaining Targeted Pathways for Biological Knowledge Bases—0
Signal Processing over Time-Varying Graphs: A Systematic Review—0
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
Explainability and Graph Learning from Social Interactions—0
Explainable and Position-Aware Learning in Digital Pathology—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified