Political Signed Temporal Networks: A Deep Learning Approach

Abstract

The evolution analysis of networks whose links are either positive or negative, representing opposite relationships such as friendship and enmity, has been revealed to be particularly useful in sociological contexts. Using a large relational dataset containing the last two centuries of state-wise geopolitical information (the correlates of war–alliance conflicts), a machine learning approach is presented to predict network dynamics. The combination of geometric as well as information–theoretic measures to characterize the resulting discrete time series together with the power of deep learning machines is used to generate a model whose predictions are even accurate on the few days in two centuries of international relations when the typical value (i.e., Alliance or Neutral) changed to a war or a conflict. In other words, the model can predict the next state of the network with a probability of error close to zero.