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 126–150 of 1570 papers

TitleStatusHype
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