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 801–825 of 1570 papers

TitleStatusHype
Graph Learning with Distributional Edge Layouts—0
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning—0
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary PatternsCode0
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
Distilling Large Language Models for Text-Attributed Graph Learning—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Can we Soft Prompt LLMs for Graph Learning Tasks?—0
Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at ScaleCode0
Revealing Decurve Flows for Generalized Graph Propagation—0
Learning Cartesian Product Graphs with Laplacian Constraints—0
Scalable Structure Learning for Sparse Context-Specific SystemsCode0
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning—0
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn AttentionCode0
Rethinking Node-wise Propagation for Large-scale Graph LearningCode0
Continual Learning on Graphs: A Survey—0
Descriptive Kernel Convolution Network with Improved Random Walk KernelCode0
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning—0
GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory—0
On the Completeness of Invariant Geometric Deep Learning ModelsCode0
Learning on Multimodal Graphs: A Survey—0
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning ApproachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified