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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 1570 papers

TitleStatusHype
Are Large Language Models In-Context Graph Learners?—0
CNN2GNN: How to Bridge CNN with GNN—0
Are Hyperbolic Representations in Graphs Created Equal?—0
Clustering with Similarity Preserving—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Flashlight: Scalable Link Prediction with Effective Decoders—0
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach—0
From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning—0
A Comprehensive Survey of Foundation Models in Medicine—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
Architectural Implications of Embedding Dimension during GCN on CPU and GPU—0
FedRGL: Robust Federated Graph Learning for Label Noise—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures—0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
EG-Gaussian: Epipolar Geometry and Graph Network Enhanced 3D Gaussian Splatting—0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning—0
Efficient Tree-based Approximation for Entailment Graph Learning—0
Causal Discovery on Dependent Binary Data—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming—0
A Primer on Temporal Graph Learning—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
_2-norm Flow Diffusion in Near-Linear Time—0
Characterizing the Influence of Topology on Graph Learning Tasks—0
Causal Graph Neural Networks for Wildfire Danger Prediction—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering—0
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Graph Contrastive Learning with Cross-view Reconstruction—0
Efficient and Stable Graph Scattering Transforms via Pruning—0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
Effective and Efficient Graph Learning for Multi-view Clustering—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
FedGIG: Graph Inversion from Gradient in Federated Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified