Learning asymmetric embedding for attributed networks via convolutional neural network. (1st June 2023)

• Record Type: Journal Article
• Authors: Radmanesh, Mohammadreza; Ghorbanzadeh, Hossein; Rezaei, Ahmad Asgharian; Jalili, Mahdi; Yu, Xinghuo
• Is Part Of: Expert systems with applications. Volume 219(2023)
• Publication Date: 2023-06-01
• Subjects: Deep network embedding -- Convolutional graph neural network -- Directed attributed networks -- Asymmetric proximity and similarity; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2023.119659
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on four real-world networks for network reconstruction, link prediction, node classification and visualization downstream tasks and investigate the impact of hyperparameters of the proposed method on the performance of the tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods.