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.

翻译：基于图的模型在理解和预测复杂网络内的导航模式方面已变得至关重要。本文在基于图的模型基础上，推进了路径外推方法，以高效预测维基百科导航路径。维基百科中马其顿（WCM）数据集源自维基百科，重点关注希腊中马其顿地区，以启动路径生成。为构建WCM，采用了一种模拟人类在维基百科中导航的爬取过程。实验表明，采用双重超图变换的图神经网络GRETEL扩展版本，在WCM的稠密图上比在WCM的稀疏图上表现更优。此外，将超图特征与从图边提取的特征相结合，已被证明能提升模型效能。模型在WCM稠密图上的性能优于在更大的Wikispeedia数据集上的表现，这表明在预测准确性方面，连接质量与特征提取可能比数据规模更具影响力。本文符合第16届国际数据库、知识与数据应用进展大会的知识发现与机器学习专题。