Collaborative Detection of Community Structure in Multiple Private Networks

Abstract

In real-world applications, each data owner might have only partial information of the complete social networks. They wish to find the community structure within multiple networks but without sharing their data directly. However, the existing works on collaborative community detection rarely consider the edges privacy issue in the networks. In this article, from the view of secure multiparty computation, we present two methods to detect the community structure of the multiple networks without directly exchanging edges’ information. These two methods are developed from the fast modularity algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$fastModular$ </tex-math></inline-formula> ) and the label propagation algorithm (LPA), and they are called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CofastModular$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CoLPA$ </tex-math></inline-formula> , respectively. Both methods can detect the community structure within multiple networks without the need to directly exchange the edges’ information. Experiments are conducted on several real-world and synthetic networks. Experimental results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CofastModular$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CoLPA$ </tex-math></inline-formula> could identify community structure effectively.