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 51–75 of 1570 papers

TitleStatusHype
Never Skip a Batch: Continuous Training of Temporal GNNs via Adaptive Pseudo-Supervision—0
Relation-Aware Graph Foundation Model—0
Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties—0
Joint Graph Estimation and Signal Restoration for Robust Federated Learning—0
Unfolded Deep Graph Learning for Networked Over-the-Air Computation—0
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Learning Kronecker-Structured Graphs from Smooth Signals—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph LearningCode0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
Partial Label ClusteringCode0
Rethinking Federated Graph Learning: A Data Condensation Perspective—0
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking—0
Multi-Scale Graph Learning for Anti-Sparse Downscaling—0
Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling—0
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach—0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs—0
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learningCode0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Edge-boosted graph learning for functional brain connectivity analysis—0
FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning—0
EG-Gaussian: Epipolar Geometry and Graph Network Enhanced 3D Gaussian Splatting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified