Dynamic network embedding via structural attention

Network embedding aims to learn low-dimensional vector representations for each node in a network, which facilitates various learning tasks such as node classification, link prediction and so on. The majority of existing embedding methods mainly focus on static networks. However, many real-world networks are dynamic and change over time. Although a small number of very recent literatures have been developed for dynamic network embedding, they either need to be retrained without closed-form expression, or suffer high-time complexity. Additionally, a large number of real-world networks may be both large and noisy, presenting great challenges to effective network representation learning. In this paper, we propose a novel method named Dynamic Network Embedding via Structural Attention (DNESA). Specifically, we incorporate the attention mechanism into network embedding, which facilitates our method mainly concentrating on task-related parts of the given graph while avoiding or ignoring noisy parts of the network. Furthermore, we can capture the evolving characteristic of dynamic networks and learn embedding vectors of each node at different time steps by modeling the process of developing an open triad into a closed triad under the attention mechanism. Meanwhile, we carefully design an optimization function for preserving both the first-order and second-order proximities. Empirical experiments conducted on six real-world networks illustrate the efficiency of the proposed method, which outperforms stateof-the-art network embedding methods in applications including link prediction and node classification.

Automated summary

The proposed Dynamic Network Embedding via Structural Attention (DNESA) method integrates attention mechanism into network embedding, focusing on task-related parts of the given graph while avoiding noisy parts, capturing the evolving characteristic of dynamic networks. Empirical experiments demonstrate the efficiency of DNESA, outperforming state-of-the-art network embedding methods in applications like link prediction and node classification.