A MOOC Course Data Analysis Based on an Improved Metapath2vec Algorithm

Abstract

Many real-world scenarios can be naturally modeled as heterogeneous graphs, which contain both symmetry and asymmetry information. How to learn useful knowledge from the graph has become one of the hot spots of research in artificial intelligence. Based on Metapath2vec algorithm, an improved Metapath2vec algorithm is presented, which combines Metapath random walk, used to capture semantics and structure information between different nodes of a heterogeneous network, and GloVe model to consider the advantage of global text representation. In order to verify the feasibility and effectiveness of the model, node clustering and link prediction experiments were conducted on the self-generated ideal dataset and the MOOC course data. The analysis of experimental data on these tasks shows that the Metapath–GloVe algorithm learns consistently better embedding of heterogeneous nodes, and the algorithm improves the node embedding performance to better characterize the heterogeneous network structure and learn the characteristics of nodes, which proves the effectiveness and scalability of the proposed method in heterogeneous network mining tasks. It is also shown through extensive experiments that the Metapath–GloVe algorithm is more efficient than the non-negative matrix decomposition algorithm (NMF), and it can obtain better clustering results and more accurate prediction results in the video recommendation task.