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 701–725 of 1570 papers

TitleStatusHype
A Survey of Data-Efficient Graph Learning—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Feature Graph Learning for 3D Point Cloud Denoising—0
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data—0
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction—0
A Study of Joint Graph Inference and Forecasting—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model—0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
Exponential Family Graph Embeddings—0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
An Uncoupled Training Architecture for Large Graph Learning—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework—0
Higher Order Structures For Graph Explanations—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified