Dynamic network representation learning based on temporal neighborhood aggregation

Fengzhe Zhong; Yan Liu; Jiaxing Fan

doi:10.1117/12.2640900

13 October 2022 Dynamic network representation learning based on temporal neighborhood aggregation

Fengzhe Zhong, Yan Liu, Jiaxing Fan

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228726 (2022) https://doi.org/10.1117/12.2640900
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

With the wide application of network data in many fields, network representation learning technology has become the focus of scholars' research. Especially in dynamic networks, how to combine network structure information with time information is the difficulty of current research. This paper proposes a dynamic network representation learning method (ESNA) based on temporal neighborhood aggregation. Further, We divide the influence of nodes on the network into local power and global power, learn the local influence of nodes on the network by using time difference attention mechanism aggregation in the temporal neighborhood, delimit the influence range of nodes on the global network by using random walk model outside the time series neighborhood, and finally fuse the local and global influence of nodes on the network based on word vector model to form a network embedded with time information. We experimented with real dynamic network data in the link prediction task, and the results showed the effectiveness of our method.

Citation Download Citation

Fengzhe Zhong, Yan Liu, and Jiaxing Fan "Dynamic network representation learning based on temporal neighborhood aggregation", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228726 (13 October 2022); https://doi.org/10.1117/12.2640900

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KEYWORDS

Social networks

Control systems

Distributed interactive simulations

Associative arrays

Fluctuations and noise

Machine learning

Network security

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