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Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

Lookup NU author(s): Stephen Bonner, John Brennan, Dr Stephen McGough

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2019, The Author(s). Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings, which could be a possible way to bring interpretability to the process. In this paper, we investigate if graph embeddings are approximating something analogous to traditional vertex-level graph features. If such a relationship can be found, it could be used to provide a theoretical insight into how graph embedding approaches function. We perform this investigation by predicting known topological features, using supervised and unsupervised methods, directly from the embedding space. If a mapping between the embeddings and topological features can be found, then we argue that the structural information encapsulated by the features is represented in the embedding space. To explore this, we present extensive experimental evaluation with five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features. We demonstrate that several topological features are indeed being approximated in the embedding space, allowing key insight into how graph embeddings create good representations.


Publication metadata

Author(s): Bonner S, Kureshi I, Brennan J, Theodoropoulos G, McGough AS, Obara B

Publication type: Article

Publication status: Published

Journal: Data Science and Engineering

Year: 2019

Pages: Epub ahead of print

Online publication date: 29/06/2019

Acceptance date: 22/06/2019

Date deposited: 16/07/2019

ISSN (print): 2364-1185

ISSN (electronic): 2364-1541

Publisher: Springer

URL: https://doi.org/10.1007/s41019-019-0097-5

DOI: 10.1007/s41019-019-0097-5


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