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 1401–1425 of 1570 papers

TitleStatusHype
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
A Simple Spectral Failure Mode for Graph Convolutional Networks—0
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection—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
Neural Algorithms for Graph Navigation—0
From Local Structures to Size Generalization in Graph Neural Networks—0
Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data—0
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks—0
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization TheoryCode0
QR and LQ Decomposition Matrix Backpropagation Algorithms for Square, Wide, and Deep -- Real or Complex -- Matrices and Their Software ImplementationCode0
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
CatGCN: Graph Convolutional Networks with Categorical Node FeaturesCode0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
Structured Graph Learning for Clustering and Semi-supervised Classification—0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint—0
Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples—0
Multivariate Relations Aggregation Learning in Social Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified