Abstract
An HDI tensor modeling a dynamic network contains rich knowledge regarding dynamic network evolution. An LFT-based model has attracted plenty of attention on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic network data, thereby leading to the descent of model representation learning ability. To tackle this problem, this chapter proposes a multiple biases-incorporated latent factorization of tensors (MBLFT) model, which incorporates preprocessing bias, short-term bias and long-term bias into an LFT-based model. Empirical studies on two large-scale dynamic networks generated by industrial applications show that the proposed MBLFT model achieves higher prediction accuracy than state-of-the-art models in solving missing link prediction task.
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Wu, H., Wu, X., Luo, X. (2023). Multiple Biases-Incorporated Latent Factorization of Tensors. In: Dynamic Network Representation Based on Latent Factorization of Tensors. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-19-8934-6_2
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DOI: https://doi.org/10.1007/978-981-19-8934-6_2
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