Existing heterogeneous graph neural networks (HGNNs) have achieved great success in utilizing the rich semantic information in heterogeneous information networks (HINs). However, few works have delved into the utilization of long-range dependencies in HINs, which is extremely valuable as many real-world HINs are sparse, and each node has only a few directly connected neighbors. Although some HGNNs can utilize distant neighbors by stacking multiple layers or leveraging long meta-paths, the exponentially increased number of nodes in the receptive field or the number of meta-paths incurs high computation and memory costs. To address these issues, we investigate the importance of different meta-paths and propose Long-range Dependency based Multi-Layer Perceptron (LDMLP). Specifically, to solve the high-cost problem of leveraging long-range dependencies, LDMLP adopts a search stage to discover effective meta-paths automatically, reducing the exponentially increased number of meta-paths to a constant. To avoid the influence of specific modules on search results, LDMLP utilizes a simple architecture with only multi-layer perceptions in the search stage, improving the generalization of searched meta-paths. As a result, the searched meta-paths not only perform well in LDMLP but also enable other HGNNs like HAN and SeHGNN to perform better. Extensive experiments on eight heterogeneous datasets demonstrate that LDMLP achieves state-of-the-art performance while enjoying high efficiency and generalization, especially on sparse HINs.

翻译：现有异构图神经网络（HGNNs）在利用异构信息网络（HINs）中的丰富语义信息方面取得了巨大成功。然而，很少有研究深入探讨HINs中长程依赖的利用，这在许多现实世界的HINs稀疏、每个节点仅有少量直接连接邻居的情况下极具价值。尽管一些HGNNs可通过堆叠多层或利用长元路径来利用远距离邻居，但感受野中节点数量或元路径数量的指数级增长会导致高昂的计算和内存成本。为解决这些问题，我们研究了不同元路径的重要性，并提出了基于长程依赖的多层感知机（LDMLP）。具体而言，为解决利用长程依赖的高成本问题，LDMLP采用搜索阶段自动发现有效元路径，将指数增长的元路径数量缩减为常数。为避免特定模块对搜索结果的影响，LDMLP在搜索阶段采用仅包含多层感知机的简单架构，从而提升所搜索元路径的泛化能力。最终，搜索得到的元路径不仅在LDMLP中表现优异，还能使其他HGNNs（如HAN和SeHGNN）性能提升。在八个异构数据集上的大量实验表明，LDMLP在实现最先进性能的同时兼具高效性和泛化能力，特别是在稀疏HINs上表现尤为突出。