Identification of influential social networkers

Online social networking is deeply interleaved in todays lifestyle. People come together and build communities to share thoughts, offer suggestions, exchange in formation, ideas, and opinions. Moreover, social networks often serve as platforms for information dissemination and product placement or promotion through viral marketing. The success rate in this type of marketing could be increased by tar geting specific individuals, called “influential users”, having the largest possible reach within an online community. In this paper we present a method aiming at identifying the influential users within an online social networking application. We introduce ProfileRank, a metric that uses popularity and activity characteristics of each user to rank them in terms of their influence. We then assess this algorithm’s added value in identifying influential users compared to other commonly used so cial network analysis metrics, such as the Betweenness Centrality and the well-known PageRank, by performing an experimental evaluation on a synthetic and a real-life data set. We also integrate all three metrics in a unified metric and measure its performance