Heterogeneous graph

eterogeneous graph representation learning is crit-

ical for analyzing complex data structures. Metapaths within this

field are vital as they elucidate high-order relationships across the

graph, significantly enhancing the model’s accuracy and depth of

understanding. However, metapaths tend to prioritize long-range

dependencies of the target node, which can lead to the oversight of

potentially crucial 1st-order heterogeneous neighbors or short-

range dependencies. To address this challenge and circumvent