Abstract by Bryn Balls-Barker
Link Prediction Using Effective Transitions
We introduce a new method for predicting the formation of links in real-world networks, which we refer to as Effective Transition. Our method uses isoradial reductions to compute the probabilities of eventually transitioning from one node to another within the network and then predicts links to form between edges with higher probabilities. Unlike the large majority of link prediction techniques, this method can be used to predict links in networks that are directed or undirected and weighted or unweighted. We apply this method to large social, technological, and natural networks and show that it is competitive with other common predictors and in a good number of cases outperforms. We also provide a method of approximating our effective transition link predictor and show that aside from having much lower computational complexity, this approximation often provides more accurate predictions than the original method.