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 476–500 of 1570 papers

TitleStatusHype
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Adversarial Attacks on Deep Graph Matching—0
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation—0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
An Uncoupled Training Architecture for Large Graph Learning—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning—0
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification—0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified