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 551–600 of 1570 papers

TitleStatusHype
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness—0
A Survey of Data-Efficient Graph Learning—0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity AlignmentCode1
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint PredictionCode1
A Survey on Structure-Preserving Graph Transformers—0
MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning—0
Multiview Graph Learning with Consensus Graph—0
LPNL: Scalable Link Prediction with Large Language Models—0
MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information LeakageCode0
Tensor-view Topological Graph Neural NetworkCode0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity—0
FedGTA: Topology-aware Averaging for Federated Graph LearningCode0
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation—0
Disentangled Condensation for Large-scale GraphsCode1
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
A Survey on Learning from Graphs with Heterophily: Recent Advances and Future DirectionsCode2
Towards Principled Graph TransformersCode1
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information AggregationCode0
A novel hybrid time-varying graph neural network for traffic flow forecasting—0
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN ExpressivenessCode1
Optimizing k in kNN Graphs with Graph Learning Perspective—0
Machine Learning on Dynamic Graphs: A Survey on Applications—0
Temporal Link Prediction Using Graph Embedding DynamicsCode0
A General Benchmark Framework is Dynamic Graph Neural Network Need—0
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic ForecastingCode0
Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
A Primer on Temporal Graph Learning—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
A Topology-aware Graph Coarsening Framework for Continual Graph Learning—0
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement PredictionCode1
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning—0
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological ImagesCode1
GraphGPT: Graph Learning with Generative Pre-trained TransformersCode1
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNsCode0
Joint Signal Recovery and Graph Learning from Incomplete Time-Series—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
LGMRec: Local and Global Graph Learning for Multimodal RecommendationCode1
Diffusion Maps for Signal Filtering in Graph Learning—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Dynamic Frequency Domain Graph Convolutional Network for Traffic ForecastingCode0
Graph Transformers for Large GraphsCode1
Multi-level graph learning for audio event classification and human-perceived annoyance rating predictionCode0
Robust Graph Neural Network based on Graph DenoisingCode0
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting—0
Show:102550
← PrevPage 12 of 32Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified