Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

微生物群落在自然界中普遍存在，对生存环境和人类健康影响很大。微生物群落对环境及其宿主的所有这些影响通常被称为这些群落的功能，这在很大程度上取决于群落的组成。对微生物高阶模块的研究可以帮助我们了解微生物群落的动态发展和进化过程，探索群落功能。考虑到传统的聚类方法依赖于簇的数量或不属于任何簇的数据的影响，本文提出一种基于博弈论的超图聚类算法来挖掘微生物高阶相互作用模块（HCGI），并超图聚类问题自然地转化为聚类博弈问题，网络模块的划分转化为寻找进化稳定性策略（ESS）的临界点。实验结果表明HCGI不依赖于类数，可以获得更保守、质量更好的微生物聚类模块，为研究人员提供参考，节省时间和成本。本文HCGI的源代码可以从https://github.com/ylm0505/HCGI下载。