Multilayer network representation learning model with structure entropy information

Yong Wu; Guanghui Yan; Hao Luo

doi:10.1117/12.2685375

21 July 2023 Multilayer network representation learning model with structure entropy information

Yong Wu, Guanghui Yan, Hao Luo

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271736 (2023) https://doi.org/10.1117/12.2685375
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Network representation learning maps network nodes to a unified vector space, which has found widespread use in various complex network analysis tasks. The resulting embedding vectors can reflect the original network's features and improve the accuracy of downstream tasks. However, current multi-layer network representation learning models have limited node sequence semantics and cannot fully capture the node's local and global structural information. To address this issue, we propose a multi-layer network representation learning model that integrates structural entropy information. Our model comprehensively considers the local and global structure of a multi-layer network, using local structure entropy and network standard structure entropy to guide the intra-layer and inter-layer migration processes, and then trains the node sequence using the skip-gram model to obtain the node vector. Experimental results on five public datasets demonstrate that our proposed model's embedding vector quality is significantly improved, with AUC scores on different datasets improving by 5% to 10% in the link prediction task.

Citation Download Citation

Yong Wu, Guanghui Yan, and Hao Luo "Multilayer network representation learning model with structure entropy information", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271736 (21 July 2023); https://doi.org/10.1117/12.2685375

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