Studying the Wikipedia Math Essential Pages using Graph Theory Metrics

Abstract COVID-19 pandemic enforced students in schools and universities all around the world to study using the online and blinded learning. In these learning models, students depend on the Internet for information searching of different scientific essentials to improve their skills and to overcome the gap of facing instructors. One of the most popular sources of information is Wikipedia. In this work, we attempt to study the relations of different math essential pages of Wikipedia to find the relation between these topics. A graph has been constructed for these pages. The graph theoretical metrics, such as, centrality, edge weights and clustering coefficient have been extracted of the constructed graph. The extracted values have been investigated to gain more insights of the math topics that should be studied first. The extracted results show that the in-degree property of the articles and the betweenness value of these articles are correlated. Moreover, there is no relation between the in /out-degree of the pages. Finally, the constructed graph has a small average shortest path and a high global cluster coefficient. This proves that the constructed graph follows the small world phenomenon. Keywords: Graph metrics, Math essentials, Gephi, Small world phenomenon, Directed graph