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
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
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
All the World's a (Hyper)Graph: A Data DramaCode1
Covariant Compositional Networks For Learning GraphsCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
State of the Art and Potentialities of Graph-level LearningCode1
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized PreferenceCode1
Data Augmentation for Deep Graph Learning: A SurveyCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Cluster-wise Graph Transformer with Dual-granularity Kernelized AttentionCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
A Simple Graph Contrastive Learning Framework for Short Text ClassificationCode1
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in HealthcareCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Generative 3D Part Assembly via Dynamic Graph LearningCode1
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal RecommendationCode1
A Survey of Cross-domain Graph Learning: Progress and Future DirectionsCode1
Deep Temporal Graph ClusteringCode1
Examining the Effects of Degree Distribution and Homophily in Graph Learning ModelsCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
Efficient Heterogeneous Graph Learning via Random ProjectionCode1
CCGL: Contrastive Cascade Graph LearningCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
An Efficient Subgraph GNN with Provable Substructure Counting PowerCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
CktGNN: Circuit Graph Neural Network for Electronic Design AutomationCode1
Non-convolutional Graph Neural NetworksCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph LearningCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN ExpressivenessCode1
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed GraphsCode1
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data AugmentationsCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
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