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 851–900 of 1570 papers

TitleStatusHype
Time-varying Signals Recovery via Graph Neural Networks—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
Equivariant Polynomials for Graph Neural Networks—0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
On the Expressivity of Persistent Homology in Graph Learning—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
G-Signatures: Global Graph Propagation With Randomized Signatures—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical StructuresCode1
Distances for Markov Chains, and Their DifferentiationCode0
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment—0
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
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems—0
Quantum Graph Learning: Frontiers and Outlook—0
Graph Neural Operators for Classification of Spatial Transcriptomics Data—0
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural NetworksCode1
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction—0
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal RecommendationCode1
Continual Graph Learning: A Survey—0
On the Connection Between MPNN and Graph TransformerCode1
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective—0
Graph Contrastive Learning for Skeleton-based Action RecognitionCode1
Causal Bandits without Graph LearningCode0
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk PredictionCode1
Explainable Multilayer Graph Neural Network for Cancer Gene PredictionCode1
GIPA: A General Information Propagation Algorithm for Graph LearningCode1
Graph Learning Across Data Silos—0
Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity PredictionCode0
State of the Art and Potentialities of Graph-level LearningCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified