جستجوی مقالات مرتبط با کلیدواژه "social networks" در نشریات گروه "مواد و متالورژی"

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping and disjoint detection of community in networks. In recent years, many researchers have concentrated on feature learning and network embedding methods for node clustering. These methods map the network into a lower-dimensional representation space. We propose a model in this research for learning graph representation using deep neural networks. In this method, a nonlinear embedding of the original graph is fed to stacked auto-encoders for learning the model. Then an overlapping clustering algorithm is performed to obtain overlapping communities. The effectiveness of the proposed model is investigated by conducting experiments on standard benchmarks and real-world datasets of varying sizes. Empirical results exhibit that the presented method outperforms some popular community detection methods.

The purpose of this research, is to study factors influencing privacy concerns about data security and protection on social network sites and its’ influence on self-disclosure. 100 articles about privacy protection, data security, information disclosure and Information leakage on social networks were studied. Models and algorithms types and their repetition in articles have been distinguished and this study builds a research model to examine privacy concerns and the effect of it on self-disclosure. The need of having knowledge and skill about privacy protection seems to be necessary with social networks technology developments. Most of the researches have been studied about privacy protection scope related to users’ privacy on social networks including women, men, children and adults in smartphone and E-health. Most of researches on this scope have been done in USA. Most studies were focused on privacy protection and security on social networks.