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 1426–1450 of 1570 papers

TitleStatusHype
Flashlight: Scalable Link Prediction with Effective Decoders—0
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning—0
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach—0
Edge-Featured Graph Attention Network—0
Foundations and Frontiers of Graph Learning Theory—0
Edge-boosted graph learning for functional brain connectivity analysis—0
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection—0
Free Lunch for Privacy Preserving Distributed Graph Learning—0
From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning—0
From Molecular Dynamics to MeshGraphNets—0
Accurately Solving Physical Systems with Graph Learning—0
From Quantum Graph Computing to Quantum Graph Learning: A Survey—0
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
Functional2Structural: Cross-Modality Brain Networks Representation Learning—0
Fund2Vec: Mutual Funds Similarity using Graph Learning—0
Future Automation Engineering using Structural Graph Convolutional Neural Networks—0
G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification—0
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
SoK: Differential Privacy on Graph-Structured Data—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified