  Graph-based models have become pivotal in understanding and predicting
navigational patterns within complex networks. Building on graph-based models,
the paper advances path extrapolation methods to efficiently predict Wikipedia
navigation paths. The Wikipedia Central Macedonia (WCM) dataset is sourced from
Wikipedia, with a spotlight on the Central Macedonia region, Greece, to
initiate path generation. To build WCM, a crawling process is used that
simulates human navigation through Wikipedia. Experimentation shows that an
extension of the graph neural network GRETEL, which resorts to dual hypergraph
transformation, performs better on a dense graph of WCM than on a sparse graph
of WCM. Moreover, combining hypergraph features with features extracted from
graph edges has proven to enhance the model's effectiveness. A superior model's
performance is reported on the WCM dense graph than on the larger Wikispeedia
dataset, suggesting that size may not be as influential in predictive accuracy
as the quality of connections and feature extraction. The paper fits the track
Knowledge Discovery and Machine Learning of the 16th International Conference
on Advances in Databases, Knowledge, and Data Applications.
