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 1151–1175 of 1570 papers

TitleStatusHype
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs—0
Towards Federated Graph Learning for Collaborative Financial Crimes Detection—0
Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation—0
Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs—0
Optimizing k in kNN Graphs with Graph Learning Perspective—0
Attributed Graph Learning with 2-D Graph Convolution—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations—0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
P3-Distributed Deep Graph Learning at Scale—0
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings—0
PANDORA: Deep graph learning based COVID-19 infection risk level forecasting—0
Towards Graph Foundation Models: A Survey and Beyond—0
Towards joint graph learning and sampling set selection from data—0
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
PatSTEG: Modeling Formation Dynamics of Patent Citation Networks via The Semantic-Topological Evolutionary Graph—0
A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings—0
A Topology-aware Graph Coarsening Framework for Continual Graph Learning—0
PerCNet: Periodic Complete Representation for Crystal Graphs—0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training—0
Towards Multi-modal Graph Large Language Model—0
Show:102550
← PrevPage 47 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified