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 651–675 of 1570 papers

TitleStatusHype
Heterogeneous Graph Learning for Visual Commonsense ReasoningCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Grasper: A Generalist Pursuer for Pursuit-Evasion ProblemsCode0
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn AttentionCode0
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRICode0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
Haar-Laplacian for directed graphsCode0
Fine-grained Graph Learning for Multi-view Subspace ClusteringCode0
CorrAdaptor: Adaptive Local Context Learning for Correspondence PruningCode0
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
Graph Structural Attack by Perturbing Spectral DistanceCode0
FedSPA: Generalizable Federated Graph Learning under Homophily HeterogeneityCode0
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training StrategyCode0
Graph Retention Networks for Dynamic GraphsCode0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
FedGTA: Topology-aware Averaging for Federated Graph LearningCode0
Graph Neural Networks with Local Graph ParametersCode0
Robust Graph Representation Learning for Local Corruption RecoveryCode0
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph LearningCode0
Graph Learning Network: A Structure Learning AlgorithmCode0
Graph learning under sparsity priorsCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Federated Graph Semantic and Structural LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified