Ergcn: Data Enhancement Based Robust Graph Convolutional Network for Node Classification

Tao Wu; Nan Yang; Long Chen; Xiaokui Xiao; Shaojie Qiao; Jun Liu; Xingping Xian

doi:10.2139/ssrn.4157770

ScienceGate Book Chapters

JOURNAL ARTICLE

Ergcn: Data Enhancement Based Robust Graph Convolutional Network for Node Classification

Tao Wu Nan Yang Long Chen Xiaokui Xiao Shaojie Qiao Jun Liu Xingping Xian

Year: 2022 Journal: SSRN Electronic Journal Publisher: RELX Group (Netherlands)

DOI: 10.2139/ssrn.4157770

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Decision boundary Adversarial system Graph Convolutional neural network Node (physics) Data mining Artificial intelligence Machine learning Theoretical computer science

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

Energy Efficient Wireless Sensor Networks

Physical Sciences → Computer Science → Computer Networks and Communications

Ergcn: Data Enhancement Based Robust Graph Convolutional Network for Node Classification

Metrics

Topics

Related Documents

ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks

Edge and Node Enhancement Graph Convolutional Network: Imbalanced Graph Node Classification Method Based on Edge-Node Collaborative Enhancement

A Kernel Propagation-Based Graph Convolutional Network Imbalanced Node Classification Model on Graph Data

A Spatial Graph Convolutional Network Based on Node Semantic for Graph Classification

Neighborhood Adaptive Graph Convolutional Network for Node Classification