Social insects are one of the best examples of complex self-organized systems exhibiting task allocation. How task allocation is achieved is the most fascinating question in behavioural ecology and complex systems science. However, it is difficult to comprehensively characterize task allocation patterns due to behavioural complexity, such as the individual variation, context dependency and chronological variation. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we applied bipartite network analyses to characterize individual-behaviour relationships. We recorded the behaviours of all individuals with verified age in ant colonies and analysed the individual-behaviour relationship at the individual, module and network levels. Bipartite network analysis successfully detected the module structures, illustrating that certain individuals performed a subset of behaviours (i.e. task groups). We confirmed age polyethism by comparing age between modules. Additionally, to test the daily rhythm of the executed tasks, the data were partitioned between daytime and nighttime, and a bipartite network was re-constructed. This analysis supported that there was no daily rhythm in the tasks performed. These findings suggested that bipartite network analyses could untangle complex task allocation patterns and provide insights into understanding the division of labour.

社会性昆虫是展示任务分配的复杂自组织系统的最佳例子之一。如何实现任务分配是行为生态学和复杂系统科学中最令人着迷的问题。然而，由于行为的复杂性，例如个体差异、情境依赖性和时间差异，很难全面表征任务分配模式。因此，必须量化个体行为并将其整合到群体水平中。在这里，我们应用二分网络分析来表征个体行为关系。我们记录了蚁群中所有已验证年龄的个体的行为，并在个体、模块和网络层面分析了个体与行为的关系。二分网络分析成功地检测到模块结构，说明某些个体执行了行为的子集（即任务组）。我们通过比较模块之间的年龄来确认年龄多民族性。此外，为了测试执行任务的日常节奏，将数据分为白天和夜间，并重新构建了二分网络。该分析表明所执行的任务没有每日节奏。这些发现表明，双向网络分析可以理清复杂的任务分配模式，并为理解劳动分工提供见解。