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 351–375 of 1570 papers

TitleStatusHype
Forward Learning of Graph Neural NetworksCode1
Disentangled Condensation for Large-scale GraphsCode1
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical StructuresCode1
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingCode1
Distance Recomputator and Topology Reconstructor for Graph Neural NetworksCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large GraphsCode1
Towards Fair Graph Neural Networks via Graph CounterfactualCode1
S^2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment AnalysisCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Beyond Message Passing: Neural Graph Pattern MachineCode1
Node Dependent Local Smoothing for Scalable Graph LearningCode1
Online Graph Dictionary LearningCode1
On the Connection Between MPNN and Graph TransformerCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Continual Learning for Smart City: A Survey—0
Continual Graph Learning: A Survey—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
A Survey of Data-Efficient Graph Learning—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
A Study of Joint Graph Inference and Forecasting—0
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified