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 501–525 of 1570 papers

TitleStatusHype
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations—0
Graph learning methods to extract empathy supporting regions in a naturalistic stimuli fMRI—0
Uncertainty in Graph Neural Networks: A Survey—0
Analysis of Total Variation Minimization for Clustered Federated Learning—0
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New BenchmarkCode1
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning—0
BloomGML: Graph Machine Learning through the Lens of Bilevel OptimizationCode0
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node EmbeddingCode0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
DNNLasso: Scalable Graph Learning for Matrix-Variate DataCode0
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
OpenGraph: Towards Open Graph Foundation ModelsCode3
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method—0
Graph Learning with Distributional Edge Layouts—0
HiGPT: Heterogeneous Graph Language ModelCode2
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary PatternsCode0
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed GraphsCode1
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
Show:102550
← PrevPage 21 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified