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 1101–1125 of 1570 papers

TitleStatusHype
Heterogeneous Trajectory Forecasting via Risk and Scene Graph LearningCode0
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling—0
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs—0
Unrolled Graph Learning for Multi-Agent Collaboration—0
Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome—0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs—0
Graph Few-shot Learning with Task-specific StructuresCode0
Learning Graphical Factor Models with Riemannian OptimizationCode0
Data-Augmented Counterfactual Learning for Bundle Recommendation—0
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis—0
One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium—0
Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning—0
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property—0
Intrinsic Dimension for Large-Scale Geometric LearningCode0
Towards Real-Time Temporal Graph LearningCode0
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
A Unified Framework for Optimization-Based Graph Coarsening—0
A Unified Framework against Topology and Class ImbalanceCode0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
Contrastive Graph Few-Shot Learning—0
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model—0
Show:102550
← PrevPage 45 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified