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 101–150 of 1570 papers

TitleStatusHype
Forward Learning of Graph Neural NetworksCode1
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New BenchmarkCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed GraphsCode1
ZeroG: Investigating Cross-dataset Zero-shot Transferability in GraphsCode1
SimMLP: Training MLPs on Graphs without SupervisionCode1
Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow DataCode1
Unifying Generation and Prediction on Graphs with Latent Graph DiffusionCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity AlignmentCode1
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint PredictionCode1
Disentangled Condensation for Large-scale GraphsCode1
Towards Principled Graph TransformersCode1
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN ExpressivenessCode1
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement PredictionCode1
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological ImagesCode1
GraphGPT: Graph Learning with Generative Pre-trained TransformersCode1
LGMRec: Local and Global Graph Learning for Multimodal RecommendationCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Graph Transformers for Large GraphsCode1
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
Environment-Aware Dynamic Graph Learning for Out-of-Distribution GeneralizationCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL DesignsCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic GraphsCode1
Efficient Heterogeneous Graph Learning via Random ProjectionCode1
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node RepresentationCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
Multimodal Graph Learning for Generative TasksCode1
GraphLLM: Boosting Graph Reasoning Ability of Large Language ModelCode1
Tailoring Self-Attention for Graph via Rooted SubtreesCode1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event ClassificationCode1
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
Where Did the Gap Go? Reassessing the Long-Range Graph BenchmarkCode1
CktGNN: Circuit Graph Neural Network for Electronic Design AutomationCode1
Scalable Incomplete Multi-View Clustering with Structure AlignmentCode1
Class-Imbalanced Graph Learning without Class RebalancingCode1
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
How Expressive are Graph Neural Networks in Recommendation?Code1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
Learning on Graphs with Out-of-Distribution NodesCode1
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
TimeGNN: Temporal Dynamic Graph Learning for Time Series ForecastingCode1
An Empirical Evaluation of Temporal Graph BenchmarkCode1
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph LearningCode1
Examining the Effects of Degree Distribution and Homophily in Graph Learning ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified