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 1501–1550 of 1570 papers

TitleStatusHype
Graph Learning Network: A Structure Learning AlgorithmCode0
Provably Powerful Graph NetworksCode0
Clustering with Similarity Preserving—0
RGB-T Image Saliency Detection via Collaborative Graph LearningCode1
Stability and Generalization of Graph Convolutional Neural Networks—0
Graph Transformer—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
Robust Graph Data Learning via Latent Graph Convolutional Representation—0
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification—0
A Unified Framework for Structured Graph Learning via Spectral ConstraintsCode0
RepGN:Object Detection with Relational Proposal Graph Network—0
Semi-Supervised Graph Classification: A Hierarchical Graph PerspectiveCode0
Graph Learning over Partially Observed Diffusion Networks: Role of Degree Concentration—0
Learning Context Graph for Person Search—0
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation—0
Low-rank Kernel Learning for Graph-based Clustering—0
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling—0
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few LabelsCode0
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User PreferencesCode0
Collaborative Similarity Embedding for Recommender SystemsCode0
Graph-RISE: Graph-Regularized Image Semantic Embedding—0
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty—0
Algebraic graph learning of protein-ligand binding affinity—0
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm—0
Robust Graph Learning from Noisy DataCode0
Inferring Networks From Random Walk-Based Node SimilaritiesCode0
Scalable Graph Learning for Anti-Money Laundering: A First LookCode0
Graph Learning-Convolutional Networks—0
A simple yet effective baseline for non-attributed graph classificationCode0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Graph Laplacian mixture modelCode0
Exploiting Edge Features in Graph Neural Networks—0
Future Automation Engineering using Structural Graph Convolutional Neural Networks—0
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts—0
Improved large-scale graph learning through ridge spectral sparsification—0
Learning graphs from data: A signal representation perspective—0
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging—0
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel IdentificationCode0
Learning Networks from Random Walk-Based Node SimilaritiesCode0
Covariant Compositional Networks For Learning GraphsCode1
Learning Typed Entailment Graphs with Global Soft ConstraintsCode0
Residual Gated Graph ConvNetsCode0
Convolutional Neural Knowledge Graph Learning—0
Large Scale Graph Learning from Smooth Signals—0
Visual Tracking via Dynamic Graph Learning—0
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Graph Classification with 2D Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified