Analysis of Social Metrics in Dynamic Networks: Measuring the Influence with FRINGE

Nowadays, social networking services, such as Facebook, Google+ or Twitter, have been drawing increasing attention from everyday users to research and industrial communities worldwide. The success of these social networking services provides a wealth of information that, suitably managed, may offer very useful knowledge to decision making. This paper overviews the state of the art of this emerging field of study and, in particular, collects several social metrics that are useful for decision making. Furthermore, the FRINGE algorithm is proposed as a method to measure the degree of influence of a network node, i. e. to what extent a certain node is isolated or is inside a community in which it is the leader or is near him or her, as a consequence of its good performance in accuracy and computational cost. To study the advantages of our proposal, we present an indepth analysis of the Flickr dataset. Our results show that there exists a strong correlation between the popularity of the set of photos uploaded by a user and his or her influence. This information may turn out to be very valuable for selective marketing campaigns, by taking advantage of the leadership structure of a social network, in order to spread a brand-new product widely throughout the network