Uncovering University Application Patterns Through Graph Representation Learning

Abstract

In university admissions, interaction networks naturally emerge between prospective students and available majors. Understanding hidden patterns in such a vast network is crucial for decision-making but poses technical challenges due to its complexity and data limitations. Many existing models rely heavily on user profiling, raising privacy concerns and making data collection difficult. Instead, this work extracts meaningful insights using only the adjacency information of the network, avoiding the need for personal data. We leverage Graph Convolutional Networks (GCN) to generate compact representations for major recommendation and clustering tasks. Our GCN-based approach outperforms classical methods such as popularity-based and Non-negative Matrix Factorization (NMF), as well as the neural Generalized Matrix Factorization (GMF) model, achieving up to 61.06% and 12.17% improvements in smaller (dimension 40) and larger (dimension 80) embeddings, respectively. Furthermore, hierarchical clustering on these embeddings reveals implicit patterns in student preferences, particularly regarding fields of study and geographic locations, even without explicit data on these attributes. These findings demonstrate that meaningful insights can be derived from interaction networks while mitigating privacy concerns associated with user profiling.