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 1526–1550 of 1570 papers

TitleStatusHype
Graph Federated Learning Based on the Decentralized Framework—0
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks—0
Graph Force Learning—0
Do graph neural network states contain graph properties?—0
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective—0
GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Distribution Preserving Graph Representation Learning—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
GraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning—0
Graphical Models in Heavy-Tailed Markets—0
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design—0
Distributed Graph Learning with Smooth Data Priors—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Distilling Large Language Models for Text-Attributed Graph Learning—0
Graph Information Matters: Understanding Graph Filters from Interaction Probability—0
Graph Intention Network for Click-through Rate Prediction in Sponsored Search—0
Universal Graph Continual Learning—0
Graph Learning—0
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey—0
Graph Learning Approaches to Recommender Systems: A Review—0
Graph Learning: A Survey—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation—0
Show:102550
← PrevPage 62 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified