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 851–875 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
Show:102550
← PrevPage 35 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified