The World Wide Web is one of the most inevitable notions in the life of mankind. In the most recent times of the world it is much more in advance gaining popularity due to its enormous amount of capability in making the life more impact-able. Online social networks are one of the areas of the World Wide Web where people congregate to share and be part of the various virtual communities. Online social networks are more fascinating to many of us now as they look out for similarly inclined people in order to share in reciprocity their findings, ideals, thoughts, opinions and views. Much like the physical human networks, cybernetics too has people who can influence or influenced by the other. Some are leaders who inevitably influence voluminous people, while others look out to get influenced or to induce inspiration from their leader. Identifying people who exercise maximum influence could be useful in targeting them for marketing, knowledge dissemination and other such purposes. In this paper, we present empirical analysis of levels of influence in the online social network. Findings of this paper may be of great help to elucidate in ascertaining leaders of a social network which in turn can be used to sight the leaders in real world. The leaders of a social network are traced by using Pareto front function. We believe that this is the first study to use Pareto front function to identify the leaders in online social networks. The empirical results prove that the number of leaders in each subsequent level monotonically increase while the number of their followers decreases.

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Social Network Measuring Influence Pareto Front Function