An Improved Performance for Dynamic Online Social Network

Data mining

Online social networks (OSNs) are an important for scientists in different fields such as computer science, sociology, economics, etc. However, it is hard to study OSNs as they are very large. For these reasons, we argue that sampling techniques will be the best technique to study OSNs in the future. Our research aims to offer performance improvements, via sampling, to the process of (almost) uniformly collecting information from user logs by exploring the underlying graph structure of a social network. Sampling methods are proposed to approximate community structures in a social network. Our work is also related to work on personalized and social search. The premise of personalized search is that by tailoring search to the individual improved result accuracy may be brought off. A vast amount of literature on search personalization reveals significant improvement over traditional web search. Our contribution is to evaluate the behavior of these techniques on a real directed graph by considering sampling scenarios.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.