Gong Changwei, Xue Bing, Jing Changhong, He Chun-Hui, Wu Guo-Cheng, Lei Baiying, Wang Shuqiang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518060, China.
Department of Computer Science, University of Chinese Academy of Sciences, Beijing 100049, China.
Math Biosci Eng. 2022 Sep 13;19(12):13276-13293. doi: 10.3934/mbe.2022621.
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.
脑社区检测是一种表示脑网络社区的有效方法。然而，大脑的时变功能和复杂的脑社区结构给它带来了巨大挑战。本文提出了一种时间序列图对抗学习（TGAL）框架，用于检测脑社区并从脑网络中刻画社区结构。在该框架中，设计了一种新颖的时间序列图神经网络作为编码器，通过时空注意力机制提取有效的图表示。由于难以捕捉社区结构，因此使用可测量的模块度损失，通过最大化社区的模块度来进行优化。此外，该框架采用对抗方案来指导表示学习。通过在真实世界脑网络数据集上的实验，展示了我们模型的有效性，脑社区检测的出色性能证明了所提出框架的优势。
