Article: Evolving network representation learning based on recurrent neural network Journal: International Journal of Sensor Networks (IJSNET) 2024 Vol.46 No.2 pp.114 - 122 Abstract: An evolving network refers to a dynamic network with edges lasting for an extended period. Typical evolving networks include friend relationship networks and employment relationship networks. The topological structure of the evolving network remains relatively stable and exhibits learnable evolution rules, making it a hot topic for research. The primary objective of representation learning in evolving networks is to extract informative content from both the temporal and spatial dimensions and represent the network as a low-dimensional embedding vector. However, existing methods for evolving network representation learning lack time-related information. In link prediction tasks, the absence of associated information within the interval between the known interaction information and the time to be predicted by link prediction hinders the acquisition of comprehensive node representations. A novel evolving network representation learning based on recurrent neural network (ENRR) is proposed to address this problem. This algorithm leverages historical interaction information and recurrent neural network predictions to obtain network association information within the specified time interval. Comparative experiments on link prediction with baselines across multiple real-world datasets demonstrate that the proposed algorithm provides significant validity and reliability. Inderscience Publishers - linking academia, business and industry through research

Title: Evolving network representation learning based on recurrent neural network

Authors: Dongming Chen; Mingshuo Nie; Qianqian Gan; Dongqi Wang

Addresses: Software College Northeastern University, Shenyang 110169, China ' Software College Northeastern University, Shenyang 110169, China ' Software College Northeastern University, Shenyang 110169, China ' Software College Northeastern University, Shenyang 110169, China

Abstract: An evolving network refers to a dynamic network with edges lasting for an extended period. Typical evolving networks include friend relationship networks and employment relationship networks. The topological structure of the evolving network remains relatively stable and exhibits learnable evolution rules, making it a hot topic for research. The primary objective of representation learning in evolving networks is to extract informative content from both the temporal and spatial dimensions and represent the network as a low-dimensional embedding vector. However, existing methods for evolving network representation learning lack time-related information. In link prediction tasks, the absence of associated information within the interval between the known interaction information and the time to be predicted by link prediction hinders the acquisition of comprehensive node representations. A novel evolving network representation learning based on recurrent neural network (ENRR) is proposed to address this problem. This algorithm leverages historical interaction information and recurrent neural network predictions to obtain network association information within the specified time interval. Comparative experiments on link prediction with baselines across multiple real-world datasets demonstrate that the proposed algorithm provides significant validity and reliability.

Keywords: network representation learning; evolving network; recurrent neural network; link prediction; node behaviour characteristics.

DOI: 10.1504/IJSNET.2024.141767

International Journal of Sensor Networks, 2024 Vol.46 No.2, pp.114 - 122

Received: 18 Mar 2024
Accepted: 29 Mar 2024
Published online: 01 Oct 2024 *

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Title: Evolving network representation learning based on recurrent neural network

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