Predicting kills in Game of Thrones using network properties

TV series such as HBO's most popular show Game of Thrones have seen a high number of dedicated followers, who watch and thoroughly analyze every minute of the show. Largely discussed aspect of the show between viewers seems to be the dramatic murders of the most important characters, the thing that the series is most known for. In our work, we try to predict characters' kills (killer and victim pairs) using data about previous kills by the characters and additional metadata. We construct a network with characters as nodes, where two nodes are linked if one killed the other. Then we use a link prediction framework and evaluate different techniques to predict the next possible kills. Lastly, we construct features from various network properties on a social network of characters, which we use in conjunction with classic data mining techniques. We see that due to the small size of the kills dataset and the somewhat random distribution of kills, we cannot predict much with standard indices. However, we show that we can construct an index which is very customized for the exact network we create for Game of Thrones but might not work on other series. We also see that the features we compute on the social network of characters help with standard machine learning approaches as well but do not yield as accurate predictions as we would hope. The best results overall are achieved by using a custom index for link prediction, which is fitting for our type of network and gives us an Area Under the ROC Curve (AUC) of 0.863.