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 601–650 of 1570 papers

TitleStatusHype
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks—0
Learning High-Dimensional Differential Graphs From Multi-Attribute Data—0
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting—0
PerCNet: Periodic Complete Representation for Crystal Graphs—0
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning—0
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning—0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs—0
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning—0
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Environment-Aware Dynamic Graph Learning for Out-of-Distribution GeneralizationCode1
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRICode0
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction—0
Mobility-Induced Graph Learning for WiFi Positioning—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL DesignsCode1
Dirichlet Active Learning—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Improving Collaborative Filtering Recommendation via Graph Learning—0
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Certified Defense on the Fairness of Graph Neural NetworksCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental LearningCode0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic GraphsCode1
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Multimodal Graph Learning for Modeling Emerging Pandemics with Big DataCode0
Efficient Heterogeneous Graph Learning via Random ProjectionCode1
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
GraphGPT: Graph Instruction Tuning for Large Language ModelsCode2
Open-World Lifelong Graph LearningCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified