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 351–400 of 1570 papers

TitleStatusHype
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive DiagnosisCode1
Disentangled Condensation for Large-scale GraphsCode1
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large GraphsCode1
Fast Optimizer BenchmarkCode1
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized PreferenceCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
Beyond Message Passing: Neural Graph Pattern MachineCode1
Node Dependent Local Smoothing for Scalable Graph LearningCode1
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022Code1
On the Connection Between MPNN and Graph TransformerCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural NetworksCode1
Graph Information Bottleneck for Subgraph RecognitionCode1
Link Prediction with Persistent Homology: An Interactive ViewCode1
SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge GraphsCode1
Continual Learning for Smart City: A Survey—0
Continual Graph Learning: A Survey—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
A Survey of Data-Efficient Graph Learning—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
A Benchmark for Fairness-Aware Graph Learning—0
A Study of Joint Graph Inference and Forecasting—0
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data—0
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model—0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
An Uncoupled Training Architecture for Large Graph Learning—0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening—0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
A Simple Spectral Failure Mode for Graph Convolutional Networks—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Computing Steiner Trees using Graph Neural Networks—0
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Scalable and Effective Alternative to Graph Transformers—0
Collaborative Interest-aware Graph Learning for Group Identification—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Show:102550
← PrevPage 8 of 32Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified