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Open AccessArticle

DOPSIE: Deep-Order Proximity and Structural Information Embedding

by Mario Manzo 1,*,† and Alessandro Rozza 2
1
Information Technology Services, University of Naples “L’Orientale”, 80121 Naples, Italy
2
lastminute.com group, 6830 Chiasso, Switzerland
*
Author to whom correspondence should be addressed.
Current address: Via Nuova Marina, 49, 80133 Naples, Italy.
Mach. Learn. Knowl. Extr. 2019, 1(2), 684-697; https://doi.org/10.3390/make1020040
Received: 13 March 2019 / Revised: 17 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
(This article belongs to the Section Network)
Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems. View Full-Text
Keywords: graph representations; vector space; classification graph representations; vector space; classification
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Manzo, M.; Rozza, A. DOPSIE: Deep-Order Proximity and Structural Information Embedding. Mach. Learn. Knowl. Extr. 2019, 1, 684-697.

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