Complex Adaptive Systems Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
Health Psychology Lab, Ghent University, Ghent, Belgium.
PLoS One. 2022 Aug 26;17(8):e0273609. doi: 10.1371/journal.pone.0273609. eCollection 2022.
Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements.
图嵌入方法近年来受到越来越多的关注，主要是因为它们具有普遍适用性。它们将网络数据转换为向量空间，在这个空间中，最大限度地保留了图的结构信息和属性。然而，大多数现有的方法忽略了节点之间丰富的交互信息，即边属性或边类型。此外，学习到的嵌入缺乏可解释性，并且不能用于研究边属性网络中类型结构的影响。在本文中，我们引入了一种将边类型信息嵌入到图节中的框架，并生成了带类型的边图节度向量（TyE-GDV）。此外，我们将两种组合方法，即彩色图节和异质图节方法扩展到边属性网络中。通过将提出的方法应用于慢性疼痛患者的案例研究，我们发现不仅患者的网络结构可以指示他/她的感知疼痛程度，而且某些社会关系，如与朋友、同事和医疗保健专业人员的关系，对于理解慢性疼痛的影响更为关键。此外，我们证明在节点分类任务中，边类型编码图节方法比传统的图节度向量方法有显著的优势，并且 TyE-GDV 在空间需求方面的效率要高得多，但在性能上可以与组合方法相媲美。
