Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
This work addresses sequence prediction in graphs for applications like website navigation and public transport, but it is incremental as it builds on existing statistical modeling frameworks.
The authors tackled the problem of predicting sequences of traversed nodes in graphs by proposing a multi-order network model that combines multiple higher-order models, achieving state-of-the-art performance in next-element prediction and scaling to millions of sequences.
We propose a novel sequence prediction method for sequential data capturing node traversals in graphs. Our method builds on a statistical modelling framework that combines multiple higher-order network models into a single multi-order model. We develop a technique to fit such multi-order models in empirical sequential data and to select the optimal maximum order. Our framework facilitates both next-element and full sequence prediction given a sequence-prefix of any length. We evaluate our model based on six empirical data sets containing sequences from website navigation as well as public transport systems. The results show that our method out-performs state-of-the-art algorithms for next-element prediction. We further demonstrate the accuracy of our method during out-of-sample sequence prediction and validate that our method can scale to data sets with millions of sequences.