Spatial Event Prediction via Multivariate Time Series Analysis of Neighboring Social Units using Deep Neural Networks

摘要

Event prediction in social network structures is a crucial research problem in social network analysis. This impels understanding the intrinsic relationship patterns preserving a given social network structure, based on the study of several structural properties computed on the constituent social units, with respect to space and time. In this regard, tackling problems of this nature is considered NP-Complete. Consequently, this paper proposes an original and unique approach which involves making event predictions about a target social unit, y, based on the intrinsic patterns of relationship learnt from one or more neighboring social units. Our methodology is based on Deep Learning (DL) architectures, and is developed using deep-layer stacks of Multilayer Perceptron (MLP) appended with an adjustment-bias (ab) vector at the output layer in a bid to improve the accuracy and precision of predictions made with respect to the target unit (or node). Also, we trained and tested our technique on a real world social clique comprising 5 connected cities; thereafter, we performed a comparative analysis of our approach against 9 other models drawn from the fields of Deep Learning, Machine Learning, and Statistics.