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 1451–1475 of 1570 papers

TitleStatusHype
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory—0
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning—0
Generalization from Starvation: Hints of Universality in LLM Knowledge Graph Learning—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—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
Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning—0
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks—0
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning—0
Multimodal learning with graphs—0
Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding—0
Dynamic Graph Learning based on Graph Laplacian—0
A Metric for the Balance of Information in Graph Learning—0
Dynamic Graph Condensation—0
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis—0
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection—0
GKAN: Graph Kolmogorov-Arnold Networks—0
GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks—0
Sparse Graph Learning Under Laplacian-Related Constraints—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified