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 1026–1050 of 1570 papers

TitleStatusHype
Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation—0
Mitigating Subpopulation Bias for Fair Network Topology Inference—0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering—0
Can we Soft Prompt LLMs for Graph Learning Tasks?—0
Mobility-Induced Graph Learning for WiFi Positioning—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer—0
Modeling Multi-Step Scientific Processes with Graph Transformer Networks—0
Supercharging Graph Transformers with Advective Diffusion—0
Molecular Classification Using Hyperdimensional Graph Classification—0
Molecule Generation for Drug Design: a Graph Learning Perspective—0
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models—0
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions—0
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification—0
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting—0
MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning—0
Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties—0
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach—0
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty—0
Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services—0
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks—0
Multilayer Clustered Graph Learning—0
Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified