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 1276–1300 of 1570 papers

TitleStatusHype
A Structural Feature-Based Approach for Comprehensive Graph Classification—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
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
A Simple Spectral Failure Mode for Graph Convolutional Networks—0
LPNL: Scalable Link Prediction with Large Language Models—0
Uncertainty in Graph Neural Networks: A Survey—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Uncertainty Quantification on Graph Learning: A Survey—0
Uncovering Insurance Fraud Conspiracy with Network Learning—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Scalable and Effective Alternative to Graph Transformers—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
Are Large Language Models In-Context Graph Learners?—0
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning—0
Understanding Graph Learning with Local Intrinsic Dimensionality—0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Are Hyperbolic Representations in Graphs Created Equal?—0
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified