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 876–900 of 1570 papers

TitleStatusHype
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery—0
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
Relation-Aware Graph Foundation Model—0
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding—0
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting—0
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
RepGN:Object Detection with Relational Proposal Graph Network—0
Representing Videos as Discriminative Sub-graphs for Action Recognition—0
ResolvNet: A Graph Convolutional Network with multi-scale Consistency—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
Rethinking Federated Graph Learning: A Data Condensation Perspective—0
Rethinking Graph Structure Learning in the Era of LLMs—0
Continual Graph Learning: A Survey—0
Continual Learning for Smart City: A Survey—0
Revealing Decurve Flows for Generalized Graph Propagation—0
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation—0
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks—0
Robust Graph Data Learning with Latent Graph Convolutional Representation—0
Continual Learning on Graphs: A Survey—0
Robust Graph Learning Under Wasserstein Uncertainty—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
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified