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 751–800 of 1570 papers

TitleStatusHype
Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving—0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
Substructure Aware Graph Neural NetworksCode1
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI—0
Directional diffusion models for graph representation learning—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Spatial-Temporal Graph Learning with Adversarial Contrastive AdaptationCode1
Globally Interpretable Graph Learning via Distribution Matching—0
STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual DiarizationCode1
Multi-Temporal Relationship Inference in Urban AreasCode0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
Explainable and Position-Aware Learning in Digital Pathology—0
Learning on Graphs under Label Noise—0
Time-aware Graph Structure Learning via Sequence Prediction on Temporal GraphsCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Expectation-Complete Graph Representations with HomomorphismsCode0
A Graph Dynamics Prior for Relational InferenceCode0
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs—0
arXiv4TGC: Large-Scale Datasets for Temporal Graph ClusteringCode0
Comprehensive evaluation of deep and graph learning on drug-drug interactions predictionCode1
Permutation Equivariant Graph Framelets for Heterophilous Graph LearningCode0
Migrate Demographic Group For Fair GNNs—0
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free DataCode1
Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation—0
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilitiesCode0
The Information Pathways Hypothesis: Transformers are Dynamic Self-EnsemblesCode1
Detecting Low Pass Graph Signals via Spectral Pattern: Sampling Complexity and Applications—0
Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks—0
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative PolynomialsCode1
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning BenchmarksCode0
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting—0
Learning Strong Graph Neural Networks with Weak InformationCode1
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer—0
Confidence-Based Feature Imputation for Graphs with Partially Known FeaturesCode1
Inductive detection of Influence Operations via Graph Learning—0
Graph Neural Convection-Diffusion with HeterophilyCode1
Continual Learning on Dynamic Graphs via Parameter IsolationCode1
Link Prediction without Graph Neural NetworksCode2
Joint Feature and Differentiable k -NN Graph Learning using Dirichlet Energy—0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Stability and Generalization of lp-Regularized Stochastic Learning for GCN—0
Graph Propagation Transformer for Graph Representation LearningCode1
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis—0
Deep Temporal Graph ClusteringCode1
Free Lunch for Privacy Preserving Distributed Graph Learning—0
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation—0
FedHGN: A Federated Framework for Heterogeneous Graph Neural NetworksCode1
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction—0
Fisher Information Embedding for Node and Graph LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified