Malaysia's Domestic Tourism Network

Abstract

Malaysia's domestic tourism can be as important as international tourism. This paper analyses Malaysia's domestic tourism network using graph theory. Data are obtained from the Domestic Tourism Survey 2023 by the Department of Statistics Malaysia. States are represented as vertices, while unweighted directed edges represent significant tourist flows between states. The study aims to identify tourism hubs through centrality measures and explore network communities using the Louvain algorithm and Fiedler spectral clustering. State performance is assessed using in-degree, out-degree, betweenness, closeness, and eigenvector centralities. States with higher in-degree centrality are shown to be popular destinations. The study also examines the Pearson correlation between centrality metrics and each state's gross domestic product (GDP) to assess the relationship between economic activity and domestic tourism. Findings show a strong positive correlation between betweenness centrality and GDP. Both the Louvain and Fiedler methods reveal clusters that are aligned with the spatial distribution of the states. These network insights provide useful information for tourism authorities in identifying key destination hubs, strengthening inter-state tourism corridors, and supporting regional tourism planning.