Triadic Closure Pattern Analysis and Prediction in Social Networks

Abstract

We study the problem of group formation in online social networks. In particular, we focus on one of the most important human groups-the triad-and try to understand how closed triads are formed in dynamic networks, by employing data from a large microblogging network as the basis of our study. We formally define the problem of triadic closure prediction and conduct a systematic investigation. The study reveals how user demographics, network characteristics, and social properties influence the formation of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in a dynamic network. Different kernel functions are incorporated into the proposed graphical model to quantify the similarity between triads. Our experimental results with the large microblogging dataset demonstrate the effectiveness (+10 percent over alternative methods in terms of F1-Score) of the proposed model for the prediction of triadic closure formation.