Gong, Changwei and Xue, Bing and Jing, Changhong and He, Chun-Hui and Wu, Guo-Cheng and Lei, Baiying and Wang, Shuqiang (2022) Time-sequential graph adversarial learning for brain modularity community detection. Mathematical Biosciences and Engineering, 19 (12). pp. 13276-13293. ISSN 1547-1063, DOI https://doi.org/10.3934/mbe.2022621.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) [62172403] [61872351], Distinguished Young Scholars Fund of Guangdong [2021B1515020019], Excellent Young Scholars of Shenzhen [RCYX20200714114641211], Shenzhen Key Basic Research Projects [JCYJ20200109115641762] |
| Uncontrolled Keywords: | Community detection; Modularity; Brain networks; Graph representation; Adversarial learning |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 15 Sep 2023 07:39 |
| Last Modified: | 15 Sep 2023 07:39 |
| URI: | http://eprints.um.edu.my/id/eprint/41264 |
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