Measuring influence in online social networks: With a focus on Gowalla and Brightkite

In the most basic sense, a network is any collection of objects in which some pairs of these objects are connected by links. In a network of objects, objects can be people or computers, which we refer to them as nodes of the network. Social links among friends play an important role in dictating their behavior. In online social networks (OSNs), these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions and more. Such flow of information determines the influence of a node. In this paper, we investigate the relative influence of a node, using large amount of data sets collected from Gowalla and Brightkite. We, initially, identify the correlated nodes in these large data sets using customized divide-and-conquer algorithm. Then, we measure the influence of each of these nodes using a linear function. Users who have more friends and who made more check-ins (an event when the time and the location of a particular user is recorded) are not necessarily having top influence to dictate friend's behavior. Also, users who have the highest influence are those whose total number of friends are closer to the average number of nodes in the network, which is the total number of friends of each node divided by the total number of nodes. We believe that these observations can help organizations to narrow down their digital marketing strategies.