research-article

Multiple Biases-incorporated Latent Factorization of Tensors for Dynamic Network Link Prediction

Authors:

Hao WuAuthors Info & Claims

ICIAI '21: Proceedings of the 2021 5th International Conference on Innovation in Artificial Intelligence

Pages 36 - 41

https://doi.org/10.1145/3461353.3461356

Published: 04 September 2021 Publication History

Abstract

The topological information of a dynamic network varies over time, making it crucial to capture its temporal patterns for predicting missing links accurately. A latent factorization of tensors (LFT)-based model has proven to be efficient to solve this problem, where a dynamic network is represented as a three-way high-dimensional and sparse (HiDS) tensor. However, currently LFT-based models do not consider multiple biases in analyzing an HiDS tensor for accomplishing dynamic link prediction. To address this issue, this paper proposes a multiple biases-incorporated latent factorization of tensors (MBLFT) model, which integrates short-term bias, preprocessing bias and long-term bias into an LFT model. Empirical studies on two large-scale dynamic networks from real applications show that compared with state-of-the-art predictors, an MBLFT model achieves higher prediction accuracy and computational efficiency for missing links in dynamic network.

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Cited By

Wu XWang LXie MShan K(2023)A Well-Designed Regularization Scheme for Latent Factorization of High-Dimensional and Incomplete Water-Quality Tensors from Sensor Networks2023 19th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN60784.2023.00089(596-603)Online publication date: 14-Dec-2023
https://doi.org/10.1109/MSN60784.2023.00089
Wu HWu XLuo XWu HWu XLuo X(2023)Multiple Biases-Incorporated Latent Factorization of TensorsDynamic Network Representation Based on Latent Factorization of Tensors10.1007/978-981-19-8934-6_2(11-26)Online publication date: 8-Mar-2023
https://doi.org/10.1007/978-981-19-8934-6_2

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Multiple Biases-incorporated Latent Factorization of Tensors for Dynamic Network Link Prediction

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cover image ACM Other conferences

ICIAI '21: Proceedings of the 2021 5th International Conference on Innovation in Artificial Intelligence

March 2021

246 pages

ISBN:9781450388634

DOI:10.1145/3461353

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 September 2021

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Natural Science Foundation of Sichuan Province of China
National Natural Science Foundation of China

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ICIAI 2021

ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence

March 5 - 8, 2021

Xia men, China

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Cited By

Wu XWang LXie MShan K(2023)A Well-Designed Regularization Scheme for Latent Factorization of High-Dimensional and Incomplete Water-Quality Tensors from Sensor Networks2023 19th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN60784.2023.00089(596-603)Online publication date: 14-Dec-2023
https://doi.org/10.1109/MSN60784.2023.00089
Wu HWu XLuo XWu HWu XLuo X(2023)Multiple Biases-Incorporated Latent Factorization of TensorsDynamic Network Representation Based on Latent Factorization of Tensors10.1007/978-981-19-8934-6_2(11-26)Online publication date: 8-Mar-2023
https://doi.org/10.1007/978-981-19-8934-6_2

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