Property graph representation learning for node classification

文献类型： 外文期刊

第一作者： Li, Shu

作者： Li, Shu;Du, Meijie;Zhou, Zhou;Zhang, Hongfei;Li, Shu;Du, Meijie;Zhou, Zhou;Zhang, Hongfei;Zaidi, Nayyar A.;Li, Gang

作者机构：

关键词： Property graph; Graph representation learning; GraphSAGE; Biased sampling; Latent connection

期刊名称：KNOWLEDGE AND INFORMATION SYSTEMS （ 影响因子：2.7； 五年影响因子：3.3 ）

ISSN： 0219-1377

年卷期： 2023 年

页码：

收录情况： SCI

摘要： 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 DeepWalk and Node2vec, focus only on the structure of graph for learning the node embedding. Although graph representation learning based on neural networks (e.g. typical GNN methods such as GraphSAGE) 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 Enhanced Property Graph Embedding (EPGE)-a graph representation learning framework to address above-mentioned limitations. Our proposed framework relies on the notion of latent neighbourhood, as well as systematic sampling of neighbouring nodes to obtain better representation of the nodes. The experimental results on five publicly available graph datasets demonstrate that EPGE outperforms state-of-the-art baselines for the task of node classification. We further evaluate the superiority of our proposed formulation by defining a novel quantitative metric to measure the usefulness of the sampled neighbourhood in the graph.

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