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 1001–1025 of 1570 papers

TitleStatusHype
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis—0
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation—0
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction—0
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering—0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training—0
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis—0
Hierarchical Transformer for Scalable Graph Learning—0
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified ModelsCode0
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees—0
Hyperbolic Geometry in Computer Vision: A Survey—0
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World—0
Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients—0
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network—0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Inductive Graph UnlearningCode0
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
Topological Pooling on GraphsCode0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks—0
Gradient scarcity with Bilevel Optimization for Graph LearningCode0
Graph Signal Processing: History, Development, Impact, and Outlook—0
A Survey on Oversmoothing in Graph Neural Networks—0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified