Home // DBKDA 2024, The Sixteenth International Conference on Advances in Databases, Knowledge, and Data Applications // View article

Constructing and Analyzing Different Density Graphs for Path Extrapolation in Wikipedia

Authors:
Martha Sotiroudi
Anastasia-Sotiria Toufa
Constantine Kotropoulos

Keywords: Wikipedia Dataset; Path Extrapolation; GRETEL; Dual Hypergraph Transformation; Graph Neural Networks.

Abstract:
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.

Pages: 12 to 19

Copyright: Copyright (c) IARIA, 2024

Publication date: March 10, 2024

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-68558-138-1

Location: Athens, Greece

Dates: from March 10, 2024 to March 14, 2024