Due to the hardship of labeling on these datasets, there are a variety of approaches on feature selection process in an unsupervised setting by considering some important characteristics of data.
Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models.
The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities.
Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms.
Besides, exploration and exploiting of semantic relations is regarded as a principal step in text mining applications.
In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model.
Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets.
Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains.