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 726–750 of 1570 papers

TitleStatusHype
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions—0
Hippocampal Spatial Mapping As Fast Graph Learning—0
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting—0
Graph Relation Aware Continual Learning—0
Graph Representation Learning for Spatial Image Steganalysis—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Graph-RISE: Graph-Regularized Image Semantic Embedding—0
Graph schemas as abstractions for transfer learning, inference, and planning—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Higher-order Structure Boosts Link Prediction on Temporal Graphs—0
A Unified Framework for Optimization-Based Graph Coarsening—0
Higher-order Structure Based Anomaly Detection on Attributed Networks—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Graph Agreement Models for Semi-Supervised Learning—0
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization—0
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning—0
Graph2Graph Learning with Conditional Autoregressive Models—0
Graph Learning Across Data Silos—0
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
Graph Transformer—0
Graph Transformers: A Survey—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified