HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
Ao Liu, Jing Chen, Ruiying Du, Cong Wu, Yebo Feng, Teng Li, Jianfeng Ma
TL;DR
HeteroSample tackles the challenge of scalable analysis on IoT-driven heterogeneous graphs by proposing a deterministic sampling framework that preserves structural heterogeneity and semantic patterns. It combines top-leader selection, balanced neighborhood expansion, and meta-path guided expansions to produce representative subgraphs efficiently. Through extensive experiments on DBLP, ACM, and IMDB, it achieves superior preservation of node/edge-type distributions and meta-path patterns while boosting downstream tasks such as link prediction, using combinations with diverse embedding and GNN methods. The approach demonstrates strong practical impact for large-scale heterogeneous graph analysis, offering a favorable balance between sample quality and computational efficiency.
Abstract
The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios such as smart cities, industrial IoT, and intelligent transportation systems. However, the scale and diversity of IoT-generated data present significant challenges, and existing methods often struggle with preserving the structural integrity and semantic richness of these complex graphs. Many current approaches fail to maintain the balance between computational efficiency and the quality of the insights generated, leading to potential loss of critical information necessary for accurate decision-making in IoT applications. We introduce HeteroSample, a novel sampling method designed to address these challenges by preserving the structural integrity, node and edge type distributions, and semantic patterns of IoT-related graphs. HeteroSample works by incorporating the novel top-leader selection, balanced neighborhood expansion, and meta-path guided sampling strategies. The key idea is to leverage the inherent heterogeneous structure and semantic relationships encoded by meta-paths to guide the sampling process. This approach ensures that the resulting subgraphs are representative of the original data while significantly reducing computational overhead. Extensive experiments demonstrate that HeteroSample outperforms state-of-the-art methods, achieving up to 15% higher F1 scores in tasks such as link prediction and node classification, while reducing runtime by 20%.These advantages make HeteroSample a transformative tool for scalable and accurate IoT applications, enabling more effective and efficient analysis of complex IoT systems, ultimately driving advancements in smart cities, industrial IoT, and beyond.