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 1476–1500 of 1570 papers

TitleStatusHype
Partial Label ClusteringCode0
PatchGT: Transformer over Non-trainable Clusters for Learning Graph RepresentationsCode0
Permutation Equivariant Graph Framelets for Heterophilous Graph LearningCode0
Noise-robust Graph Learning by Estimating and Leveraging Pairwise InteractionsCode0
Prompt-Driven Continual Graph LearningCode0
Provably Powerful Graph NetworksCode0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
QR and LQ Decomposition Matrix Backpropagation Algorithms for Square, Wide, and Deep -- Real or Complex -- Matrices and Their Software ImplementationCode0
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet ConvolutionCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Random Features Strengthen Graph Neural NetworksCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Random Walk Guided Hyperbolic Graph DistillationCode0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
RecipeRec: A Heterogeneous Graph Learning Model for Recipe RecommendationCode0
RegExplainer: Generating Explanations for Graph Neural Networks in Regression TasksCode0
Regularization of Mixture Models for Robust Principal Graph LearningCode0
Relational Graph Learning for Crowd NavigationCode0
RELIANT: Fair Knowledge Distillation for Graph Neural NetworksCode0
Repeat-Aware Neighbor Sampling for Dynamic Graph LearningCode0
Residual Gated Graph ConvNetsCode0
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion FunctionalsCode0
Rethinking Node-wise Propagation for Large-scale Graph LearningCode0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering PerspectiveCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified