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 1001–1025 of 1570 papers

TitleStatusHype
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting—0
Data-centric Graph Learning: A Survey—0
Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data—0
Dataset Condensation with Latent Quantile Matching—0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning—0
Topology-aware Tensor Decomposition for Meta-graph Learning—0
Adversarial Attacks on Deep Graph Matching—0
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models—0
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search—0
THeGCN: Temporal Heterophilic Graph Convolutional Network—0
Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning—0
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach—0
Theoretically Expressive and Edge-aware Graph Learning—0
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals—0
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis—0
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction—0
Time-Series Graph Network for Sea Surface Temperature Prediction—0
Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines—0
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution—0
Time-varying Graph Learning Under Structured Temporal Priors—0
Time-Varying Graph Learning with Constraints on Graph Temporal Variation—0
Time-varying Signals Recovery via Graph Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified