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 101–150 of 1570 papers

TitleStatusHype
Embedding Words in Non-Vector Space with Unsupervised Graph LearningCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic PredictionCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Approximate Network Motif Mining Via Graph LearningCode1
A Practical, Progressively-Expressive GNNCode1
Covariant Compositional Networks For Learning GraphsCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
All the World's a (Hyper)Graph: A Data DramaCode1
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
State of the Art and Potentialities of Graph-level LearningCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
DE-HNN: An effective neural model for Circuit Netlist representationCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Data Augmentation for Deep Graph Learning: A SurveyCode1
Cluster-wise Graph Transformer with Dual-granularity Kernelized AttentionCode1
A Simple Graph Contrastive Learning Framework for Short Text ClassificationCode1
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in HealthcareCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
A Survey of Cross-domain Graph Learning: Progress and Future DirectionsCode1
Generative Contrastive Graph Learning for RecommendationCode1
Evaluating and Improving Graph-based Explanation Methods for Multi-Agent CoordinationCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
CCGL: Contrastive Cascade Graph LearningCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
Efficient Heterogeneous Graph Learning via Random ProjectionCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data AugmentationsCode1
Non-convolutional Graph Neural NetworksCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
An Efficient Subgraph GNN with Provable Substructure Counting PowerCode1
Distance Recomputator and Topology Reconstructor for Graph Neural NetworksCode1
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN ExpressivenessCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
CktGNN: Circuit Graph Neural Network for Electronic Design AutomationCode1
Dynamically Expandable Graph Convolution for Streaming RecommendationCode1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
CaseLink: Inductive Graph Learning for Legal Case RetrievalCode1
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph LearningCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified