Predicting international Facebook ties through cultural homophily and other factors

This study describes the structure of the international Facebook friendship network and its determinants using various predictors, including physical proximity, cultural homophily, and communication. Network analysis resulted in one group of nations, with countries that bridge geographic and linguistic clusters (France, Spain, United Kingdom, and United Arab Emirates) being the most central. Countries with international Facebook friendship ties tended to share borders, language, civilization, and migration. Physical distance, shared hyperlinks, use of common websites, telephone traffic, cultural similarity, and international student exchange were either weakly or not significantly related to international Facebook friendships.

Research on Combined Prediction Model for Busy Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.