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 451–500 of 1570 papers

TitleStatusHype
FairGT: A Fairness-aware Graph TransformerCode0
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
CNN2GNN: How to Bridge CNN with GNN—0
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction—0
Uncertainty Quantification on Graph Learning: A Survey—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
Introducing Graph Learning over Polytopic Uncertain Graph—0
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning—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
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
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
DE-HNN: An effective neural model for Circuit Netlist representationCode1
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
MAPL: Model Agnostic Peer-to-peer LearningCode0
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting—0
Instruction-based Hypergraph Pretraining—0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Tensor-based Graph Learning with Consistency and Specificity for Multi-view ClusteringCode0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural NetworkCode0
CaseLink: Inductive Graph Learning for Legal Case RetrievalCode1
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions—0
Graphs Generalization under Distribution Shifts—0
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual ConnectionsCode1
Segment Anything Model for Road Network Graph ExtractionCode3
Mitigating Subpopulation Bias for Fair Network Topology Inference—0
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space ModelCode3
Molecular Classification Using Hyperdimensional Graph Classification—0
Graph Unitary Message Passing—0
Forward Learning of Graph Neural NetworksCode1
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs—0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified