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 1251–1275 of 1570 papers

TitleStatusHype
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy—0
Rethinking Federated Graph Learning: A Data Condensation Perspective—0
Rethinking Graph Structure Learning in the Era of LLMs—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting—0
Revealing Decurve Flows for Generalized Graph Propagation—0
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation—0
Trustworthy Graph Neural Networks: Aspects, Methods and Trends—0
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks—0
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection—0
A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications—0
A Survey of Graph Transformers: Architectures, Theories and Applications—0
Robust Graph Data Learning with Latent Graph Convolutional Representation—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation—0
Robust Graph Learning Under Wasserstein Uncertainty—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
Weakly Supervised Graph Clustering—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
A Survey of Data-Efficient Graph Learning—0
A Study of Joint Graph Inference and Forecasting—0
A Comprehensive Survey of Foundation Models in Medicine—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified