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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 1570 papers

TitleStatusHype
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks—0
FairGT: A Fairness-aware Graph TransformerCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Uncertainty Quantification on Graph Learning: A Survey—0
CNN2GNN: How to Bridge CNN with GNN—0
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction—0
A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications—0
CKGConv: General Graph Convolution with Continuous KernelsCode1
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling—0
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching—0
Grasper: A Generalist Pursuer for Pursuit-Evasion ProblemsCode0
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices ApproachCode0
Node Similarities under Random Projections: Limits and Pathological Cases—0
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning—0
Introducing Graph Learning over Polytopic Uncertain Graph—0
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
Characterizing the Influence of Topology on Graph Learning Tasks—0
Spectral GNN via Two-dimensional (2-D) Graph Convolution—0
Model Selection with Model Zoo via Graph LearningCode0
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph TransformersCode0
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
Continual Learning for Smart City: A Survey—0
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified