提出首个异构图结构学习方法，从数据生成过程建模提升关系识别准确率。
Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
- 基于隐马尔可夫网络构建异构图数据生成模型
- 在真实与合成数据上实现高精度边类型识别与权重恢复
- 适合处理多类型节点和边的复杂网络分析任务
从观测数据中推断图结构是图机器学习的核心任务，用于捕捉数据实体间的内在关系。尽管同质图结构学习已取得显著进展，但许多现实世界图具有异构特性，即节点和边包含多种类型。本文首次提出异构图结构学习（HGSL）方法。首先，我们构建了异构图数据生成过程（DGP）的新型统计模型——隐马尔可夫网络异构图（H2MN）。随后，将HGSL形式化为基于该DGP的最大后验估计问题，并推导出交替优化算法求解，同时提供优化条件的理论证明。最后，在合成与真实数据集上进行广泛实验，结果表明所提方法在边类型识别和边权重恢复方面均表现优异。
原文摘要 · Abstract (English)
Inferring the graph structure from observed data is a key task in graph machine learning to capture the intrinsic relationship between data entities. While significant advancements have been made in learning the structure of homogeneous graphs, many real-world graphs exhibit heterogeneous patterns where nodes and edges have multiple types. This paper fills this gap by introducing the first approach for heterogeneous graph structure learning (HGSL). To this end, we first propose a novel statistical model for the data-generating process (DGP) of heterogeneous graph data, namely hidden Markov networks for heterogeneous graphs (H2MN). Then we formalize HGSL as a maximum a-posterior estimation problem parameterized by such DGP and derive an alternating optimization method to obtain a solution together with a theoretical justification of the optimization conditions. Finally, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate that our proposed method excels in learning structure on heterogeneous graphs in terms of edge type identification and edge weight recovery.
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