Inference on historical factions based on multi-layered network of historical figures.

• We identify historical factions using multi-layered network of historical figures. • Multi-layered network of historical figures is constructed using family relations. • Semi-supervised learning for multi-layered networks is used to identify the factions. • The identification benefits from matrix sparseness incurred from historical data. • Experiments show that the proposed method can provide meaningful insights on history. With immense influx of historical data, quantitative inferences on history based on machine learning is becoming more prevalent, attracting many researchers. In particular, understanding the dynamics of historical factions is important as they shared academic beliefs, political views and interests, in which the interactions between the factions portray general political, social, and economic structure of a certain era. In recent years, studying such dynamics through network-based methods on human networks, constructed from genealogy data, have shown promising results. In this paper, we enhance the identification of historical factions by exploiting multi-layered network of historical figures. To understand the mechanisms of historical factions, it is pivotal to comprehend the change in relation between important historical events. The proposed method consists of constructing a multi-layered network of historical figures and applying semi-supervised learning framework to identify historical factions. The proposed method was applied to the classification of factions in the political turmoil occurred during the 15th to 16th century Korea. [ABSTRACT FROM AUTHOR]