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 976–1000 of 1570 papers

TitleStatusHype
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Globally Interpretable Graph Learning via Distribution Matching—0
Multi-Temporal Relationship Inference in Urban AreasCode0
Learning on Graphs under Label Noise—0
Explainable and Position-Aware Learning in Digital Pathology—0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
A Graph Dynamics Prior for Relational InferenceCode0
Expectation-Complete Graph Representations with HomomorphismsCode0
arXiv4TGC: Large-Scale Datasets for Temporal Graph ClusteringCode0
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs—0
Permutation Equivariant Graph Framelets for Heterophilous Graph LearningCode0
Migrate Demographic Group For Fair GNNs—0
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
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
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning BenchmarksCode0
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting—0
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer—0
Inductive detection of Influence Operations via Graph Learning—0
Joint Feature and Differentiable k -NN Graph Learning using Dirichlet Energy—0
Stability and Generalization of lp-Regularized Stochastic Learning for GCN—0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Free Lunch for Privacy Preserving Distributed Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified