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 726–750 of 1570 papers

TitleStatusHype
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning—0
AGALE: A Graph-Aware Continual Learning Evaluation FrameworkCode0
Graph Neural Networks for Brain Graph Learning: A SurveyCode0
Learning on Large Graphs using Intersecting CommunitiesCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Graph Condensation for Open-World Graph Learning—0
FUGNN: Harmonizing Fairness and Utility in Graph Neural NetworksCode0
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection—0
Repeat-Aware Neighbor Sampling for Dynamic Graph LearningCode0
Learning Geospatial Region Embedding with Heterogeneous Graph—0
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model—0
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models—0
Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Disttack: Graph Adversarial Attacks Toward Distributed GNN TrainingCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Federated Graph Condensation with Information Bottleneck Principles—0
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks—0
FairGT: A Fairness-aware Graph TransformerCode0
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks—0
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
CNN2GNN: How to Bridge CNN with GNN—0
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction—0
Uncertainty Quantification on Graph Learning: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified