Multiscale Structural Information-Based Laplacian Generative Adversarial Network Representation Learning

In recent years, network representation learning (NRL) has attracted increasing attention due to its efficiency and effectiveness to analyze network structural data. NRL aims to learn low-dimensional representations of nodes while preserving their structural information, and preserving multiscale structural information of nodes is important for NRL. Deep learning-based algorithms are popular owing to their good performance to learn network representations, but they lack sufficient interpretability as closed boxes. In this study, we propose a novel algorithm called Multiscale structural information-based Laplacian generative adversarial Network Representation Learning (MLNRL). This algorithm consists of two components: 1) multiscale structural information preserving component, where a shift positive pointwise mutual information matrix (SPPMI) is calculated for storing multiscale structural information; 2) Laplacian generative adversarial learning component, where the ideas of Laplacian pyramid and generative adversarial networks are leveraged to generate robust and meaningful representations. We apply our model to three downstream tasks on real-world datasets for evaluation, and the results show that our model outperforms the baselines in almost all cases. Then, we perform an ablation study and verified the necessity of both components. We also investigate the hyperparameter sensitivity to prove the robustness of MLNRL.