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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\u2014e.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\u2014i.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\u00a0 ()\u2014a 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  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.\n<\/jats:p>","DOI":"10.1007\/s10115-023-01963-x","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T13:02:26Z","timestamp":1692882146000},"page":"237-265","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Property graph representation learning for node classification"],"prefix":"10.1007","volume":"66","author":[{"given":"Shu","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nayyar A.","family":"Zaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meijie","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"1963_CR1","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. 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The authors further confirm that the order of authors listed in the manuscript has been approved by all of us.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}