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 701–725 of 1570 papers

TitleStatusHype
Graph Learning with Loss-Guided Training—0
Graph Lifelong Learning: A Survey—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Graph Masked Language Models—0
Algebraic graph learning of protein-ligand binding affinity—0
Graph Mining under Data scarcity—0
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning—0
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Spectral Maps for Learning on Subgraphs—0
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation—0
A Unified Framework for Optimization-Based Graph Coarsening—0
Distilling Large Language Models for Text-Attributed Graph Learning—0
GUNDAM: Aligning Large Language Models with Graph Understanding—0
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting—0
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model—0
Feature Interaction-aware Graph Neural Networks—0
Graph Agreement Models for Semi-Supervised Learning—0
Graph Neural Operators for Classification of Spatial Transcriptomics Data—0
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified