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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1551–1570 of 1570 papers

Title	Date	Tasks	Status
Graph learning under sparsity priors	Jul 18, 2017	Graph Learning	CodeCode Available
Mixed Graphical Models for Causal Analysis of Multi-modal Variables	Apr 9, 2017	feature selectionGraph Learning	CodeCode Available
Cost-Optimal Learning of Causal Graphs	Mar 8, 2017	Graph Learning	—Unverified
Graph Learning from Data under Structural and Laplacian Constraints	Nov 16, 2016	Computational EfficiencyGraph Learning	CodeCode Available
Learning heat diffusion graphs	Nov 4, 2016	Graph Learning	—Unverified
Indirect Gaussian Graph Learning beyond Gaussianity	Oct 8, 2016	Graph Learning	—Unverified
Learning Sparse Graphs Under Smoothness Prior	Sep 12, 2016	DenoisingGraph Learning	—Unverified
Graph Construction with Label Information for Semi-Supervised Learning	Jul 8, 2016	graph constructionGraph Learning	—Unverified
Cross-Graph Learning of Multi-Relational Associations	May 6, 2016	Graph LearningRelational Reasoning	—Unverified
How to learn a graph from smooth signals	Jan 11, 2016	Graph Learning	CodeCode Available
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data	Dec 19, 2015	Graph Learning	—Unverified
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation	Dec 9, 2015	Graph EmbeddingGraph Learning	—Unverified
Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation	Jun 1, 2015	graph constructionGraph Learning	—Unverified
Semi-supervised Data Representation via Affinity Graph Learning	Feb 13, 2015	Graph Learning	—Unverified
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection	Feb 3, 2015	Graph Learning	—Unverified
On The Effect of Hyperedge Weights On Hypergraph Learning	Oct 24, 2014	ClusteringGraph Learning	—Unverified
Learning manifold to regularize nonnegative matrix factorization	Oct 3, 2014	feature selectiongraph construction	—Unverified
Learning Laplacian Matrix in Smooth Graph Signal Representations	Jun 30, 2014	Graph Learning	CodeCode Available
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach	Dec 1, 2012	BIG-bench Machine LearningClustering	—Unverified
Efficient Tree-based Approximation for Entailment Graph Learning	Jul 1, 2012	Graph LearningNatural Language Inference	—Unverified

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	HaloGraphNet	R^2	0.97	—	Unverified