Journal's Facebook pageHow Much Topological Structure Is Preserved by Graph Embeddings?
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology
AIST Waterfront ANNEX, 2-4-7 Aomi, Koto-ku, Tokyo 135-0064 Japan
{xin.liu, zhuang.chenyi, ks.kim, n.kertkeidkachorn}@aist.go.jp - Department of Computer Science, Tokyo Institute of Technology
2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552 Japan
murata@c.titech.ac.jp
Abstract
Graph embedding aims at learning representations of nodes in a low dimensional vector space. Good embeddings should preserve the graph topological structure. To study how much such structure can be preserved, we propose evaluation methods from four aspects: 1) How well the graph can be reconstructed based on the embeddings, 2) The divergence of the original link distribution and the embedding-derived distribution, 3) The consistency of communities discovered from the graph and embeddings, and 4) To what extent we can employ embeddings to facilitate link prediction. We find that it is insufficient to rely on the embeddings to reconstruct the original graph, to discover communities, and to predict links at a high precision. Thus, the embeddings by the state-of-the-art approaches can only preserve part of the topological structure.
Key words
graph embedding, network representation learning, graph reconstruction, dimension reduction, graph mining
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS123456789X
Publication information
Volume 16, Issue 2 (June 2019)
Year of Publication: 2019
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Liu, X., Zhuang, C., Murata, T., Kim, K., Kertkeidkachorn, N.: How Much Topological Structure Is Preserved by Graph Embeddings?. Computer Science and Information Systems, Vol. 16, No. 2, 597-614. (2019), https://doi.org/10.2298/CSIS123456789X
