Next Article in Journal
Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
Previous Article in Journal
DDC Control Techniques for Three-Phase BLDC Motor Position Control
 
 
Article

Properties of Vector Embeddings in Social Networks

Department of Computer Science and Mathematics, University of Passau, 94032 Passau, Germany
*
Author to whom correspondence should be addressed.
Algorithms 2017, 10(4), 109; https://doi.org/10.3390/a10040109
Received: 15 July 2017 / Revised: 4 September 2017 / Accepted: 11 September 2017 / Published: 27 September 2017
(This article belongs to the Special Issue Algorithms in Online Social Networks)
Embedding social network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification, node clustering, link prediction and network visualization. However, the information contained in these vector embeddings remains abstract and hard to interpret. Methods for inspecting embeddings usually rely on visualization methods, which do not work on a larger scale and do not give concrete interpretations of vector embeddings in terms of preserved network properties (e.g., centrality or betweenness measures). In this paper, we study and investigate network properties preserved by recent random walk-based embedding procedures like node2vec, DeepWalk or LINE. We propose a method that applies learning to rank in order to relate embeddings to network centralities. We evaluate our approach with extensive experiments on real-world and artificial social networks. Experiments show that each embedding method learns different network properties. In addition, we show that our graph embeddings in combination with neural networks provide a computationally efficient way to approximate the Closeness Centrality measure in social networks. View Full-Text
Keywords: graph embedding; network property; social network analysis graph embedding; network property; social network analysis
Show Figures

Figure 1

MDPI and ACS Style

Salehi Rizi, F.; Granitzer, M. Properties of Vector Embeddings in Social Networks. Algorithms 2017, 10, 109. https://doi.org/10.3390/a10040109

AMA Style

Salehi Rizi F, Granitzer M. Properties of Vector Embeddings in Social Networks. Algorithms. 2017; 10(4):109. https://doi.org/10.3390/a10040109

Chicago/Turabian Style

Salehi Rizi, Fatemeh, and Michael Granitzer. 2017. "Properties of Vector Embeddings in Social Networks" Algorithms 10, no. 4: 109. https://doi.org/10.3390/a10040109

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop