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

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Showing 826–850 of 1570 papers

TitleStatusHype
PatSTEG: Modeling Formation Dynamics of Patent Citation Networks via The Semantic-Topological Evolutionary Graph—0
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
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
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
FedGTA: Topology-aware Averaging for Federated Graph LearningCode0
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation—0
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information AggregationCode0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
A novel hybrid time-varying graph neural network for traffic flow forecasting—0
Machine Learning on Dynamic Graphs: A Survey on Applications—0
Optimizing k in kNN Graphs with Graph Learning Perspective—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified