Performance Analysis of User Influence Algorithm under Big Data Processing Framework in Social Networks

Social influence plays an essential role in spreading information within online social networks, and can be modeled or measured by analyzing various social networking data, such as published content, users' attributes or interactions among them. Because of the massive social data, researchers often fail to quantify user influence in an accurate and high efficient way. Big data technique can be adopted to alleviate this problem. In this paper, we introduce a kind of classical individual influence algorithm, and implement two parallel versions of this algorithm based on different big data processing framework Experiment results on a large-scale real dataset demonstrate that the computational efficiency of influence algorithm can be improved significantly in massive data sets by virtue of big data processing framework