解决异质图中语义异质性问题，提升无监督表示学习效果
When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning
- 通过融合全局结构与属性构建细粒度同质/异质潜在图
- 提出自适应双频语义融合机制，应对节点级语义异质性
- 适用于大规模真实场景，适合图学习研究者参考
无监督异质图表示学习（UHGRL）因在无标签实际图中的重要性而受到越来越多关注。然而，尽管真实异质图普遍存在异质性，该问题却长期被忽视。本文定义了语义异质性，并提出一种创新框架——潜在图引导的无监督表示学习（LatGRL），以应对该挑战。首先，我们设计了一种耦合全局结构与属性的相似性挖掘方法，构建细粒度的同质与异质潜在图，用于指导表示学习。此外，提出自适应双频语义融合机制，解决节点级语义异质性问题。为应对真实数据的大规模特性，进一步设计了可扩展实现。在基准数据集上的大量实验验证了所提框架的有效性与效率。源代码与数据集已公开于 https://github.com/zxlearningdeep/LatGRL。
原文摘要 · Abstract (English)
Unsupervised heterogeneous graph representation learning (UHGRL) has gained increasing attention due to its significance in handling practical graphs without labels. However, heterophily has been largely ignored, despite its ubiquitous presence in real-world heterogeneous graphs. In this paper, we define semantic heterophily and propose an innovative framework called Latent Graphs Guided Unsupervised Representation Learning (LatGRL) to handle this problem. First, we develop a similarity mining method that couples global structures and attributes, enabling the construction of fine-grained homophilic and heterophilic latent graphs to guide the representation learning. Moreover, we propose an adaptive dual-frequency semantic fusion mechanism to address the problem of node-level semantic heterophily. To cope with the massive scale of real-world data, we further design a scalable implementation. Extensive experiments on benchmark datasets validate the effectiveness and efficiency of our proposed framework. The source code and datasets have been made available at https://github.com/zxlearningdeep/LatGRL.
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