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
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