Academic Network Representation Learning Based on Metapath Tree
Wei Zhang, Ying Liang, Xiangxiang Dong · Journal of Physics Conference Series · 2019
Abstract Network representation learning aims to use low-dimensional dense vectors to represent nodes in the graph, which can reflect the graph structure and can be used in a variety of machine learning tasks. The academic network contains richer information, which most of the current methods are unable to capture. This paper proposes a method to get better vectors in the academic network. This method first uses the metapath tree to guide the random walk process, and adds a sampling process to preserve multiple metapath information. The vector representation of the nodes is obtained by training using the skip-gram model on the academic network. Experiment results show that the proposed model outperforms several traditional network representation learning models in multi-label classification and clustering tasks.