Professor Michael W. Berry,
Director, Center for Intelligent Systems and Machine Learning,
College of Engineering, University of Tennessee, Knoxville, TN, USA

Collaborator: Denise K. Gosnell of PokitDok, Inc., Charleston, SC, USA

Title: Soft Computing Techniques for the Modeling of Social Networks

Abstract:

Users of social media interact with the network and its users. Each interaction creates network specific data between the engaged users and the medium of communication. Over time, these engagements accumulate to describe the user's social fingerprint, an identity which encapsulates the user's persona on the network. The agglomeration of this information showcases the user's activity on the social network and establishes a traceable social fingerprint. These fingerprints can be tracked and stored as large matrices representing the quantity and occurrence of observed user behavior. We demonstrate how large-scale approximate matrix factorization techniques can be used to create a signature component vector of the social network user's identity. The results presented show that a user's social finger-print is both quantifiable and identifiable on a social network through time.