FastSGG: Efficient Social Graph Generation Using a Degree Distribution Generation Model

Author(s):  
Chaokun Wang ◽  
Binbin Wang ◽  
Bingyang Huang ◽  
Shaoxu Song ◽  
Zai Li
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.


Author(s):  
Carl Yang ◽  
Haonan Wang ◽  
Ke Zhang ◽  
Liang Chen ◽  
Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting designated noise to the gradients of a link reconstruction based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33013-33022
Author(s):  
Lucija Petricioli ◽  
Luka Humski ◽  
Mihaela Vranic ◽  
Damir Pintar

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6622-6636 ◽  
Author(s):  
Luka Humski ◽  
Damir Pintar ◽  
Mihaela Vranic

2009 ◽  
Vol 129 (9) ◽  
pp. 1690-1698
Author(s):  
Manabu Gouko ◽  
Naoki Tomi ◽  
Tomoaki Nagano ◽  
Koji Ito
Keyword(s):  

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