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 901–925 of 1570 papers

TitleStatusHype
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting—0
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning—0
Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Contrastive Graph Few-Shot Learning—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
LPNL: Scalable Link Prediction with Large Language Models—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Convolutional Neural Knowledge Graph Learning—0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure—0
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning—0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces—0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis—0
Self-supervised Graph Learning for Occasional Group Recommendation—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
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified