Utilizing long-range dependency, though extensively studied in homogeneous graphs, is rarely studied in large-scale heterogeneous information networks (HINs), whose main challenge is the high costs and the difficulty in utilizing effective information. To this end, we investigate the importance of different meta-paths and propose an automatic framework for utilizing long-range dependency in HINs, called Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, to discover meta-paths for various datasets or tasks without prior, we develop a search space with all target-node-related meta-paths. With a progressive sampling algorithm, we dynamically shrink the search space with hop-independent time complexity, leading to a compact search space driven by the current HIN and task. Utilizing a sampling evaluation strategy as the guidance, we conduct a specialized and expressive meta-path selection. Extensive experiments on eight heterogeneous datasets demonstrate that LMSPS discovers effective long-range meta-paths and outperforms state-of-the-art models. Besides, it ranks top-1 on the leaderboards of ogbn-mag in Open Graph Benchmark.

翻译：利用长程依赖关系在同质图中已被广泛研究，但在大规模异构信息网络（HIN）中却鲜有探索，其主要挑战在于高计算成本及有效信息的利用难度。为此，我们研究了不同元路径的重要性，并提出了一种名为"渐进式采样长程元路径搜索"（LMSPS）的自动化框架，用于在HIN中利用长程依赖关系。具体而言，为在无先验知识条件下发现适用于不同数据集或任务的元路径，我们构建了包含所有与目标节点相关的元路径的搜索空间。通过渐进式采样算法，我们以与跳数无关的时间复杂度动态收缩搜索空间，形成由当前HIN和任务驱动的紧凑搜索空间。借助采样评估策略作为引导，我们实现了专业且具表达力的元路径选择。在八个异构数据集上的大量实验表明，LMSPS能有效发现长程元路径，并优于现有最先进模型。此外，该方法在开放图基准（OGB）的ogbn-mag排行榜上位列第一。