PREDICTING LINK STRENGTH IN ONLINE SOCIAL NETWORKS

Abstract

Social Media is a term that encompasses the platforms of New Media, but also implies the inclusion of systems like Facebook, and other things typically thought of as social networking. The idea is that they are media platforms with social components and public communication channels. Social media are primarily Internetbased tools for sharing and discussing information among human beings. Data mining (the analysis step of the “Knowledge Discovery in Databases” process, or KDD), is the process that attempts to discover patterns in large data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. It involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, postprocessing of discovered structures, visualization, and online updating. Link prediction in Facebook and Twitter can be done at a familiar class of graph generation model, where the nodes are united with locations in a latent metric space and connections are more likely between closer nodes. In this paper, Gephi tool is used to predict the link of Facebook.