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
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models—0
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors—0
Online Multi-modal Root Cause Analysis—0
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization—0
Federated Graph Learning for Cross-Domain Recommendation—0
Generalization from Starvation: Hints of Universality in LLM Knowledge Graph Learning—0
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering—0
How to Make LLMs Strong Node Classifiers?—0
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems—0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
Graph-Based Representation Learning of Neuronal Dynamics and BehaviorCode0
GUNDAM: Aligning Large Language Models with Graph Understanding—0
One Node Per User: Node-Level Federated Learning for Graph Neural Networks—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity RecognitionCode0
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks—0
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph LearningCode0
Online Proximal ADMM for Graph Learning from Streaming Smooth Signals—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning—0
Online Learning Of Expanding GraphsCode0
Online Network Inference from Graph-Stationary Signals with Hidden Nodes—0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming—0
MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
Towards Faster Graph Partitioning via Pre-training and Inductive InferenceCode0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
Disentangled Generative Graph Representation Learning—0
LLM-enhanced Scene Graph Learning for Household Rearrangement—0
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks—0
Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs—0
Asymmetric Graph Error Control with Low Complexity in Causal Bandits—0
E-CGL: An Efficient Continual Graph LearnerCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
GrassNet: State Space Model Meets Graph Neural Network—0
CorrAdaptor: Adaptive Local Context Learning for Correspondence PruningCode0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified