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 676–700 of 1570 papers

TitleStatusHype
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction—0
Neuromorphic Imaging and Classification with Graph Learning—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
TouchUp-G: Improving Feature Representation through Graph-Centric FinetuningCode0
GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy ChangesCode0
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event ClassificationCode1
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?—0
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis—0
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
A Versatile Graph Learning Approach through LLM-based Agent—0
Human Learning of Hierarchical Graphs—0
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning—0
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries—0
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection—0
Where Did the Gap Go? Reassessing the Long-Range Graph BenchmarkCode1
Efficient Multi-View Graph Clustering with Local and Global Structure PreservationCode0
Scalable Incomplete Multi-View Clustering with Structure AlignmentCode1
CktGNN: Circuit Graph Neural Network for Electronic Design AutomationCode1
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting—0
Structural Node Embeddings with Homomorphism Counts—0
Universal Graph Continual Learning—0
Class-Imbalanced Graph Learning without Class RebalancingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified