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 301–325 of 1570 papers

TitleStatusHype
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning—0
Online Network Inference from Graph-Stationary Signals with Hidden Nodes—0
Online Learning Of Expanding GraphsCode0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
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
OpenFGL: A Comprehensive Benchmark for Federated Graph LearningCode1
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
AnyGraph: Graph Foundation Model in the WildCode3
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
Multi-task Heterogeneous Graph Learning on Electronic Health RecordsCode1
Joint Graph Rewiring and Feature Denoising via Spectral ResonanceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified