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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 1570 papers

TitleStatusHype
Graph schemas as abstractions for transfer learning, inference, and planning—0
Efficient Graph Laplacian Estimation by Proximal NewtonCode0
Semi-decentralized Federated Ego Graph Learning for Recommendation—0
Outlier-Robust Gromov-Wasserstein for Graph DataCode0
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements—0
Quantum Graph Learning: Frontiers and Outlook—0
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems—0
Graph Neural Operators for Classification of Spatial Transcriptomics Data—0
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs—0
Continual Graph Learning: A Survey—0
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective—0
Causal Bandits without Graph LearningCode0
Graph Learning Across Data Silos—0
Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity PredictionCode0
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction—0
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural NetworkCode0
RELIANT: Fair Knowledge Distillation for Graph Neural NetworksCode0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Regularized Primitive Graph Learning for Unified Vector Mapping—0
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering—0
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View ClusteringCode0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
Homophily modulates double descent generalization in graph convolution networksCode0
Recommending on graphs: a comprehensive review from a data perspective—0
Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal—0
Graph Learning with Localized Neighborhood Fairness—0
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Adversarial Weight Perturbation Improves Generalization in Graph Neural NetworksCode0
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation—0
Multi-view Graph Convolutional Networks with Differentiable Node Selection—0
An open unified deep graph learning framework for discovering drug leadsCode0
Stars: Tera-Scale Graph Building for Clustering and Graph Learning—0
Joint graph learning from Gaussian observations in the presence of hidden nodesCode0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
Architectural Implications of Embedding Dimension during GCN on CPU and GPU—0
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces—0
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation—0
Latent Graph Inference using Product Manifolds—0
PatchGT: Transformer over Non-trainable Clusters for Learning Graph RepresentationsCode0
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning ParadigmCode0
DeepGAR: Deep Graph Learning for Analogical ReasoningCode0
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
Heterogeneous Graph Sparsification for Efficient Representation Learning—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Learning Product Graphs from Spectral Templates—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified