Abstract Social interaction plays a crucial role in human societies, encompassing complex dynamics among individuals. To understand social interaction at the neural level, researchers have utilized hyperscanning in several social settings. These studies have mainly focused on inter-brain synchrony and the efficiency of paired functional brain networks, examining group interactions in dyads. However, this approach may not fully capture the complexity of multiple interactions, potentially leading to gaps in understanding inter-network differences. To overcome this limitation, the present study aims to bridge this gap by introducing methodological enhancements using the multilayer network approach, which is tailored to extract features from multiple networks. We applied this strategy to analyze the triad condition during social behavior processes to identify group interaction indices. Additionally, to validate our methodology, we compared the multilayer networks of triad conditions with group synchrony to paired conditions without group synchrony, focusing on statistical differences between alpha and beta waves. Correlation analysis between inter-brain and group networks revealed that this methodology accurately reflects the characteristics of actual behavioral synchrony. The findings of our study suggest that measures of paired brain synchrony and group interaction may exhibit distinct trends, offering valuable insights into interpreting group synchrony.