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Differentially Private Graph Neural Networks for Link Prediction
Xun Ran
, Qingqing Ye
*
, Haibo Hu
,
Xin Huang
,
Jianliang Xu
, Jie Fu
*
Corresponding author for this work
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
11
Citations (Scopus)
Overview
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Dive into the research topics of 'Differentially Private Graph Neural Networks for Link Prediction'. Together they form a unique fingerprint.
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Keyphrases
Differentially Private
100%
Link Prediction
100%
Graph Neural Network
100%
Differential Privacy
42%
Subgraph Extraction
42%
Data Dependency
28%
Extraction Methods
28%
Highly Effective
14%
Prediction Accuracy
14%
Benchmark Dataset
14%
Privacy Protection
14%
Privacy-preserving
14%
Data Privacy
14%
User Data
14%
User Interaction
14%
Privacy Techniques
14%
Noise Sensitivity
14%
Prediction Framework
14%
Degree of Dependence
14%
Utility Loss
14%
Node Level
14%
Link Prediction Problem
14%
Few-layer
14%
Framework Building
14%
Computer Science
Link Prediction
100%
Graph Neural Network
100%
Subgraphs
57%
Differential Privacy
42%
Data Dependency
28%
Privacy Preserving
14%
Data Privacy
14%
Privacy Protection
14%
Prediction Accuracy
14%
User Data
14%
User Interaction
14%
Privacy Technique
14%
Prediction Framework
14%
Core Component
14%
Noise Sensitivity
14%