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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[1] rui wang, "LSRFN", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/g1xq-8j07. Accessed: Dec. 06, 2024.
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title = {LSRFN},
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rui wang. (2024). LSRFN. IEEE Dataport. http://dx.doi.org/10.21227/g1xq-8j07
rui wang, 2024. LSRFN. Available at: http://dx.doi.org/10.21227/g1xq-8j07.
rui wang. (2024). "LSRFN." Web.
1. rui wang. LSRFN [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/g1xq-8j07
rui wang. "LSRFN." doi: 10.21227/g1xq-8j07