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 951–975 of 1570 papers

TitleStatusHype
Learning Kronecker-Structured Graphs from Smooth Signals—0
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening—0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
Learning manifold to regularize nonnegative matrix factorization—0
A Benchmark for Fairness-Aware Graph Learning—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Learning on Graphs under Label Noise—0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
Theoretically Expressive and Edge-aware Graph Learning—0
Learning on Multimodal Graphs: A Survey—0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters—0
Learning Product Graphs from Spectral Templates—0
Learning Product Graphs Underlying Smooth Graph Signals—0
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals—0
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection—0
Learning Sparse Graphs Under Smoothness Prior—0
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals—0
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Computing Steiner Trees using Graph Neural Networks—0
Learning Time-Varying Graphs from Online Data—0
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified