A fuzzy attributed graph approach to subcircuit extraction problem

2004 ◽  
Author(s):  
Nian Zhang ◽  
D.C. Wunsch
Keyword(s):  
Attributed Graph ◽  
Subcircuit Extraction

An Efficient Method for Attributed Graph Clustering

2014 ◽  
Vol 36 (8) ◽  
pp. 1704-1713 ◽  
Author(s):  
Ye WU ◽  
Zhi-Nong ZHONG ◽  
Wei XIONG ◽  
Luo CHEN ◽  
Ning JING
Keyword(s):  
Efficient Method ◽  
Graph Clustering ◽  
Attributed Graph

Searching Target Communities with Outliers in attributed graph

2021 ◽  
pp. 107622
Author(s):  
Qingqing Li ◽  
Huifang Ma ◽  
Ju Li ◽  
Zhixin Li ◽  
Yanbing Jiang
Keyword(s):  
Attributed Graph

scholarly journals Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

2020 ◽  
Vol 2020 (4) ◽  
pp. 131-152 ◽  
Author(s):  
Xihui Chen ◽  
Sjouke Mauw ◽  
Yunior Ramírez-Cruz
Keyword(s):  
Community Structure ◽  
Differential Privacy ◽  
Original Graph ◽  
Attributed Graph ◽  
Attributed Graphs ◽  
Social Graphs ◽  
Private Computation ◽  
Novel Method ◽  
Publishing Community ◽  
New Community

AbstractWe present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients.

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