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 376–400 of 1570 papers

TitleStatusHype
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model—0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
An Uncoupled Training Architecture for Large Graph Learning—0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
A Benchmark for Fairness-Aware Graph Learning—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening—0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
A Simple Spectral Failure Mode for Graph Convolutional Networks—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
Computing Steiner Trees using Graph Neural Networks—0
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Scalable and Effective Alternative to Graph Transformers—0
Collaborative Interest-aware Graph Learning for Group Identification—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified