Open AccessTime-sequential graph adversarial learning for brain modularity community detection
Author(s) -
Changwei Gong,
Bing Xue,
Changhong Jing,
ChunHui He,
Guo–Cheng Wu,
Baiying Lei,
Shuqiang Wang
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022621
Subject(s) - modularity (biology) , computer science , graph , adversarial system , community structure , artificial intelligence , representation (politics) , machine learning , theoretical computer science , mathematics , genetics , combinatorics , politics , political science , law , biology
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.