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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 1570 papers

TitleStatusHype
Deep Graph Learning for Anomalous Citation Detection—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Graph Classification with 2D Convolutional Neural Networks—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Deep Graph Clustering via Mutual Information Maximization and Mixture Model—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Graphical Models in Heavy-Tailed Markets—0
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design—0
Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Graph Neural Operators for Classification of Spatial Transcriptomics Data—0
Graph Information Matters: Understanding Graph Filters from Interaction Probability—0
Graph-RISE: Graph-Regularized Image Semantic Embedding—0
Algebraic graph learning of protein-ligand binding affinity—0
Graph Learning—0
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey—0
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization—0
Graph Learning: A Survey—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation—0
Graph Neural Networks for Graphs with Heterophily: A Survey—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
A Unified Framework for Optimization-Based Graph Coarsening—0
Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges—0
Graph Neural Modeling of Network Flows—0
Show:102550
← PrevPage 27 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified