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 1151–1175 of 1570 papers

TitleStatusHype
BScNets: Block Simplicial Complex Neural NetworksCode0
Distributed Graph Learning with Smooth Data Priors—0
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation LearningCode1
HyFactor: Hydrogen-count labelled graph-based defactorization AutoencoderCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
Self-supervised Graph Learning for Occasional Group Recommendation—0
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph LearningCode0
Relational Graph Learning for Grounded Video Description Generation—0
Pooling by Sliced-Wasserstein EmbeddingCode1
Graphical Models in Heavy-Tailed Markets—0
Structure-Aware Random Fourier Kernel for Graphs—0
Accurately Solving Rod Dynamics with Graph Learning—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
Sparse Graph Learning Under Laplacian-Related Constraints—0
Inferring halo masses with Graph Neural NetworksCode1
Spectral Transform Forms Scalable TransformerCode0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph LearningCode1
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation—0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Graph Structural Attack by Perturbing Spectral DistanceCode0
Deconvolutional Networks on Graph Data—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified