Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational costs while maximizing effective information utilization in the presence of heterogeneity, and overcoming the over-smoothing issue in graph neural networks. To address this gap, we investigate the importance of different meta-paths and introduce an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, we develop a search space with all meta-paths related to the target node type. By employing a progressive sampling algorithm, LMSPS dynamically shrinks the search space with hop-independent time complexity. Through a sampling evaluation strategy, LMSPS conducts a specialized and effective meta-path selection, leading to retraining with only effective meta-paths, thus mitigating costs and over-smoothing.

Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational costs while maximizing effective information utilization in the presence of heterogeneity, and overcoming the over-smoothing issue in graph neural networks. To address this gap, we investigate the importance of different meta-paths and introduce an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, we develop a search space with all meta-paths related to the target node type. By employing a progressive sampling algorithm, LMSPS dynamically shrinks the search space with hop-independent time complexity. Through a sampling evaluation strategy, LMSPS conducts a specialized and effective meta-path selection, leading to retraining with only effective meta-paths, thus mitigating costs and over-smoothing.

Utilizing long-range dependency, though extensively studied in homogeneous graphs, has not been well investigated on heterogeneous graphs. Addressing this research gap presents two major challenges. The first is to alleviate computational costs while endeavoring to leverage as much effective information as possible in the presence of heterogeneity. The second involves overcoming the well-known over-smoothing issue occurring in various graph neural networks. To this end, we investigate the importance of different meta-paths and introduce an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, we develop a search space with all meta-paths related to the target node type. By employing a progressive sampling algorithm, LMSPS dynamically shrinks the search space with hop-independent time complexity. Utilizing a sampling evaluation strategy as the guidance, LMSPS conducts a specialized and effective meta-path selection. Subsequently, only effective meta-paths are employed for retraining to reduce costs and overcome the over-smoothing issue. Extensive experiments on various heterogeneous datasets demonstrate that LMSPS discovers effective long-range meta-paths and outperforms the state-of-the-art. Besides, it ranks top-1 on the leaderboards of \texttt{ogbn-mag} in Open Graph Benchmark. Our code is available at https://github.com/JHL-HUST/LDMLP.