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 1201–1225 of 1570 papers

TitleStatusHype
GRAND++: Graph Neural Diffusion with A Source Term—0
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting—0
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation—0
Learning through structure: towards deep neuromorphic knowledge graph embeddingsCode1
Blindness to Modality Helps Entailment Graph MiningCode0
Graph Learning for Cognitive Digital Twins in Manufacturing Systems—0
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields—0
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs—0
Dynamic Attentive Graph Learning for Image RestorationCode1
A Study of Joint Graph Inference and Forecasting—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
roadscene2vec: A Tool for Extracting and Embedding Road Scene-GraphsCode1
Joint Graph Learning and Matching for Semantic Feature CorrespondenceCode0
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)Code1
Computing Steiner Trees using Graph Neural Networks—0
Effective and Efficient Graph Learning for Multi-view Clustering—0
P3-Distributed Deep Graph Learning at Scale—0
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Recognizing Multimodal Entailment—0
RGL-NET: A Recurrent Graph Learning framework for Progressive Part AssemblyCode1
CCGL: Contrastive Cascade Graph LearningCode1
HW2VEC: A Graph Learning Tool for Automating Hardware SecurityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified