<p>Individuals with equal status or strength are more likely to interact within the same region or field, leading to self-interested gameplay. In real life, nodes exhibit heterogeneous statuses or classes, categorized as small nodes (low-status) and big nodes (high-status). This study analyzes cooperative behaviors by modeling games between status-homogeneous nodes with networked evolutionary games. This paper employed temporal random networks to simulate interaction dynamics and investigate the effects of payoff factors. One type of network consists of small nodes representing low-status individuals, while the other is made up of big nodes representing high-status individuals. These temporal networks evolve dynamically, with node counts and edge numbers changing over time. The experiments show that punishment enhances cooperation among big nodes (high-status individuals). Cooperative behaviors differ between node types: big nodes are more likely to cooperate with their peers, exhibiting higher cooperation levels compared to small nodes.</p>