Dynamic network embedding via structural attention. (15th August 2021)

• Record Type: Journal Article
• Authors: Zhang, Chen; Fan, Yiming; Xie, Yu; Yu, Bin; Li, Chunyi; Pan, Ke
• Is Part Of: Expert systems with applications. Volume 176(2021)
• Publication Date: 2021-08-15
• Subjects: Dynamic network -- Attention mechanism -- Network embedding; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114895
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose a novel dynamic network embedding model with structural attention. Our model can capture the evolving characteristic of dynamic networks. The proposed method models the process of developing an open triad into a closed triad. The learned representations can preserve both the first-order and second-order proximities. The experimental results demonstrate the efficiency of our method. Abstract: 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 state-of-the-art network embedding methods in applications including link prediction and node classification.