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 1351–1375 of 1570 papers

TitleStatusHype
An Uncertainty-Driven GCN Refinement Strategy for Organ SegmentationCode1
Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning MethodsCode3
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Adversarial Attacks on Deep Graph Matching—0
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
World Model as a Graph: Learning Latent Landmarks for PlanningCode1
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization—0
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach—0
Node-Centric Graph Learning from Data for Brain State Identification—0
Learning on Attribute-Missing GraphsCode1
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery—0
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages—0
FiGLearn: Filter and Graph Learning using Optimal Transport—0
Multilayer Clustered Graph Learning—0
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection—0
A Simple Spectral Failure Mode for Graph Convolutional Networks—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Iterative Graph Self-Distillation—0
Learning Multi-layer Graphs and a Common Representation for Clustering—0
Self-supervised Graph Learning for RecommendationCode1
Neural Algorithms for Graph Navigation—0
From Local Structures to Size Generalization in Graph Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified