Evolving network representation learning based on recurrent neural network
by Dongming Chen; Mingshuo Nie; Qianqian Gan; Dongqi Wang
International Journal of Sensor Networks (IJSNET), Vol. 46, No. 2, 2024

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.

Online publication date: Tue, 01-Oct-2024

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