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 851–875 of 1570 papers

TitleStatusHype
When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty—0
A General Benchmark Framework is Dynamic Graph Neural Network Need—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Influential Simplices Mining via Simplicial Convolutional Network—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
Globally Interpretable Graph Learning via Distribution Matching—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Instruction-based Hypergraph Pretraining—0
Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model—0
Integrating Graphs with Large Language Models: Methods and Prospects—0
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Convolutional Neural Knowledge Graph Learning—0
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking—0
Interpretable Graph Learning Over Sets of Temporally-Sparse Data—0
Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning—0
Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation—0
Interpreting Graph Neural Networks via Unrevealed Causal Learning—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
Introducing Graph Learning over Polytopic Uncertain Graph—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified