Graph Transfer Learning and VS-Graph: Knowledge Transferable Graph Neural Networks

This article reviews recent advances in graph transfer learning, introduces the novel VS-Graph scenario for knowledge transfer between dominant and silent nodes, and details the Knowledge Transferable Graph Neural Network (KTGNN) framework with domain‑adaptive feature completion, message passing, and transferable classifier modules, highlighting experimental results and future research directions.

Knowledge TransferVS-Graphai

0 likes · 27 min read

Graph Transfer Learning and VS-Graph: Knowledge Transferable Graph Neural Networks

AntTech

Sep 29, 2022 · Artificial Intelligence

Privacy-Preserving Vertical Federated Graph Neural Network for Node Classification

This article presents VFGNN, a privacy‑preserving vertical federated graph neural network designed for node classification, detailing its architecture, differential‑privacy enhancements, and experimental results that demonstrate superior accuracy over single‑party baselines across multiple graph datasets.

Federated LearningVertical Partitiondifferential privacy

0 likes · 14 min read

Privacy-Preserving Vertical Federated Graph Neural Network for Node Classification

Code DAO

Dec 25, 2021 · Artificial Intelligence

Understanding Graph Neural Networks: Nodes, Edges, and Message Passing

This article explains the fundamentals of graph neural networks, covering graph concepts, node classification via neighborhood aggregation, message‑passing mechanics, mathematical notation, a full DGL‑PyTorch implementation on the Reddit dataset, and training results showing accuracy improvements up to 91 %.

DGLGCNGNN

0 likes · 9 min read

Understanding Graph Neural Networks: Nodes, Edges, and Message Passing

DataFunTalk

Oct 10, 2021 · Artificial Intelligence

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Overview, Challenges, and Experimental Insights

This article presents an in‑depth overview of the Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN), explains the two main limitations of conventional GNNs—lack of generality across homophilic and heterophilic graphs and over‑smoothing—describes the GPR‑GNN architecture with learnable propagation weights, and summarizes synthetic and real‑world experiments that demonstrate its superior generality, resistance to over‑smoothing, interpretability, and potential future extensions.

GNNGeneralizationGeneralized PageRank

0 likes · 18 min read

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Overview, Challenges, and Experimental Insights

DataFunSummit

Oct 9, 2021 · Artificial Intelligence

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Solving Generality and Over‑Smoothing in Graph Neural Networks

This article presents the Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN), explains the two main limitations of existing GNNs—lack of generality across homophilic and heterophilic graphs and the over‑smoothing problem—and demonstrates through synthetic and real‑world experiments that GPR‑GNN achieves robust node classification while remaining interpretable and parameter‑efficient.

GPR-GNNICLROver‑smoothing

0 likes · 18 min read

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Solving Generality and Over‑Smoothing in Graph Neural Networks