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 551–600 of 1570 papers

TitleStatusHype
Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation—0
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static RelationsCode0
Node Classification With Integrated Reject Option—0
Graph Learning for Planning: The Story Thus Far and Open Challenges—0
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment—0
HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment—0
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering PerspectiveCode0
Signal Processing over Time-Varying Graphs: A Systematic Review—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
Federated Continual Graph LearningCode0
Scale Invariance of Graph Neural NetworksCode0
Towards Data-centric Machine Learning on Directed Graphs: a Survey—0
FedRGL: Robust Federated Graph Learning for Label Noise—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
Haar-Laplacian for directed graphsCode0
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate InferenceCode0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation—0
Towards Federated Graph Learning in One-shot Communication—0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
Graph Retention Networks for Dynamic GraphsCode0
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs—0
ScaleNet: Scale Invariance Learning in Directed GraphsCode0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
Federated Graph Learning with Graphless Clients—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Shedding Light on Problems with Hyperbolic Graph Learning—0
A Survey on Kolmogorov-Arnold Network—0
Distributed-Order Fractional Graph Operating NetworkCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional NetworksCode0
Do graph neural network states contain graph properties?—0
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning—0
Online Graph Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation—0
PageRank Bandits for Link PredictionCode0
Network Games Induced Prior for Graph Topology Learning—0
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial—0
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting—0
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete DataCode0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Benchmarking Graph Learning for Drug-Drug Interaction Prediction—0
Homomorphism Counts as Structural Encodings for Graph LearningCode0
Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified