Overlapping community discovery based on core nodes and LDA topic modeling

Abstract

This paper proposed an overlapping community discovery method based on cored nodes and the Latent Allocation Dirichlet (LDA) topic modeling, which is called as CN-LDA. CN-LDA models the complex network with the LDA model, finds the probability of each edge in each community, and then uses statistical methods to calculate the probability value of each node in each community. Furthermore, to determine the community number of the network, we give an algorithm to identify the core nodes of the complex networks with the threshold random walk. CN-LDA also can be used to determine overlapping nodes. We do experiments to Compare CN-LDA with some other community algorithms in several real-world social networks. The experimental results showed that CN-LDA is effective to discover overlapping communities.

A hierarchical structure with improved OMP sparse representation used with face recognition

“…It transforms the principal component reduced the dimensionality in order to obtain the largest between-class scatter and inter-class scatter. This method is still the mainstream of face recognition method and produces a lot of different variants [18][19][20][21][22][23][24][25][26][27][28][29]. Another important method of face recognition is elastic graph matching (EGM) techniques [30], which uses an attribute graph to describe human face.…”