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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Temporal Network Embedding with High-Order Nonlinear Information

February 1, 2023

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Authors

Zhenyu Qiu

Wuhan University


Wenbin Hu

Wuhan University


Jia Wu

Macquarie University


Weiwei Liu

Wuhan University


Bo Du

Wuhan University


Xiaohua Jia

City University of Hong Kong


DOI:

10.1609/aaai.v34i04.5993


Abstract:

Temporal network embedding, which aims to learn the low-dimensional representations of nodes in temporal networks that can capture and preserve the network structure and evolution pattern, has attracted much attention from the scientific community. However, existing methods suffer from two main disadvantages: 1) they cannot preserve the node temporal proximity that capture important properties of the network structure; and 2) they cannot represent the nonlinear structure of temporal networks. In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. Specifically, we define three orders of temporal proximities by exploring network historical information with a time exponential decay model to quantify the temporal proximity between nodes. Then, we propose a novel deep guided auto-encoder to capture the highly nonlinear structure. Meanwhile, the training set of the guide auto-encoder is generated by the temporal random walk (TRW) algorithm. By training the proposed deep guided auto-encoder with a specific mini-batch stochastic gradient descent algorithm, HNIP can efficiently preserves the temporal proximities and highly nonlinear structure of temporal networks. Experimental results on four real-world networks demonstrate the effectiveness of the proposed method.

Topics: AAAI

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HOW TO CITE:

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia Temporal Network Embedding with High-Order Nonlinear Information Proceedings of the AAAI Conference on Artificial Intelligence (2020) 5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia Temporal Network Embedding with High-Order Nonlinear Information AAAI 2020, 5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia (2020). Temporal Network Embedding with High-Order Nonlinear Information. Proceedings of the AAAI Conference on Artificial Intelligence, 5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia. Temporal Network Embedding with High-Order Nonlinear Information. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia. 2020. Temporal Network Embedding with High-Order Nonlinear Information. "Proceedings of the AAAI Conference on Artificial Intelligence". 5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia. (2020) "Temporal Network Embedding with High-Order Nonlinear Information", Proceedings of the AAAI Conference on Artificial Intelligence, p.5436-5443

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia, "Temporal Network Embedding with High-Order Nonlinear Information", AAAI, p.5436-5443, 2020.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia. "Temporal Network Embedding with High-Order Nonlinear Information". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia. "Temporal Network Embedding with High-Order Nonlinear Information". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 5436-5443.

Zhenyu Qiu||Wenbin Hu||Jia Wu||Weiwei Liu||Bo Du||Xiaohua Jia. Temporal Network Embedding with High-Order Nonlinear Information. AAAI[Internet]. 2020[cited 2023]; 5436-5443.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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