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 901–925 of 1570 papers

TitleStatusHype
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint—0
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals—0
Keyframe-Focused Visual Imitation Learning—0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation—0
Continual Learning on Graphs: A Survey—0
Knowledge Probing for Graph Representation Learning—0
Multimodal Graph Learning for Deepfake Detection—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning—0
How to Make LLMs Strong Node Classifiers?—0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs—0
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining—0
Large Scale Graph Learning from Smooth Signals—0
Continual Learning for Smart City: A Survey—0
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning—0
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model—0
Continual Graph Learning: A Survey—0
Latent Graph Inference using Product Manifolds—0
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Accelerated Graph Learning from Smooth Signals—0
Latent-Graph Learning for Disease Prediction—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified