Property graph representation learning for node classification

作者：Li Shu, Nayyar A. Zaidi, Meijie Du, Zhou Zhou, Hongfei Zhang, Gang Li · 发表于：Knowledge and Information Systems · 年份：2023 · DOI：10.1007/s10115-023-01963-x · 被引用次数：10 · 研究领域：Advanced Graph Neural Networks、Complex Network Analysis Techniques

Abstract Graph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node classification, link prediction and recommendation. Many real-world graphs can be characterized as the property graphs, because besides the structure information, there exists rich property information related to each node in the graphs. Many existing graph representation learning methods—e.g. random walk-based methods like and , focus only on the structure of graph for learning the node embedding. Although graph representation learning based on neural networks (e.g. typical methods such as ) uses the property of nodes as the initial features of nodes and then aggregates feature information of the neighbours, their limitation is that the neighbourhood of a node is considered to be uniform—i.e. there is no way to differentiate among neighbours of a node when learning a node embedding. Additionally, their definition of neighbourhood is local, i.e. only nodes connected to the current node are considered as neighbours. Hence, those methods fail to capture implicit/latent relationships among nodes, which are implicit in the given structure. In this study, our aim is to improve the performance of graph representation learning methods on property graphs. We present a new framework called ()—a graph representation learning framework to address above-mentioned limitations. Our proposed framework relies on the notion of latent neighb...