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 1451–1475 of 1570 papers

TitleStatusHype
Mixed Graphical Models for Causal Analysis of Multi-modal VariablesCode0
Model Selection with Model Zoo via Graph LearningCode0
MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced InterpretationCode0
Multi-graph Fusion for Multi-view Spectral ClusteringCode0
Multi-level graph learning for audio event classification and human-perceived annoyance rating predictionCode0
Multimodal Graph Learning for Modeling Emerging Pandemics with Big DataCode0
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide PropertiesCode0
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse DomainsCode0
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few LabelsCode0
Multi-Temporal Relationship Inference in Urban AreasCode0
NaFM: Pre-training a Foundation Model for Small-Molecule Natural ProductsCode0
Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder ApproachCode0
Neural Subgraph Isomorphism CountingCode0
Next Level Message-Passing with Hierarchical Support GraphsCode0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning DatasetsCode0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Online Learning Of Expanding GraphsCode0
On the Completeness of Invariant Geometric Deep Learning ModelsCode0
On the Surprising Behaviour of node2vecCode0
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph TransformersCode0
Open-World Lifelong Graph LearningCode0
Outlier-Robust Gromov-Wasserstein for Graph DataCode0
PageRank Bandits for Link PredictionCode0
Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at ScaleCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified