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 1351–1375 of 1570 papers

TitleStatusHype
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Exploiting Edge Features for Graph Neural Networks—0
Exploiting Edge Features in Graph Neural Networks—0
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting—0
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation—0
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning—0
Exploring Edge Disentanglement for Node Classification—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Higher Order Structures For Graph Explanations—0
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering—0
Exponential Family Graph Embeddings—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
Plain Transformers Can be Powerful Graph Learners—0
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified