Time-sequential graph adversarial learning for brain modularity community detection

Changwei Gong; Bing Xue; Changhong Jing; Chun-Hui He; Guo-Cheng Wu; Baiying Lei; Shuqiang Wang; Changwei Gong; Bing Xue; Changhong Jing; Chun-Hui He; Guo-Cheng Wu; Baiying Lei; Shuqiang Wang

doi:10.3934/mbe.2022621

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 13276-13293. doi: 10.3934/mbe.2022621

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Time-sequential graph adversarial learning for brain modularity community detection

1.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518060, China
2.
Department of Computer Science, University of Chinese Academy of Sciences, Beijing 100049, China
3.
Faculty of Computer science, University of Malaya, Malaya
4.
School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
5.
Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641100, China
6.
School of Biomedical Engineering, Shenzhen University, Shenzhen 518060, China

Academic Editor: Zhichang Zhang

Received: 04 August 2022 Revised: 01 September 2022 Accepted: 01 September 2022 Published: 13 September 2022

Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.
- community detection,
- modularity,
- brain networks,
- graph representation,
- adversarial learning
Citation: Changwei Gong, Bing Xue, Changhong Jing, Chun-Hui He, Guo-Cheng Wu, Baiying Lei, Shuqiang Wang. Time-sequential graph adversarial learning for brain modularity community detection[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13276-13293. doi: 10.3934/mbe.2022621

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Abstract

Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.

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