Embedding-centrality: Generic centrality computation using neural networks

Rami Puzis; Zion Sofer; Dvir Cohen; Matan Hugi

doi:10.1007/978-3-319-73198-8_8

Embedding-centrality: Generic centrality computation using neural networks

Rami Puzis
, Zion Sofer
, Dvir Cohen
, Matan Hugi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Deriving vector representations of vertices in graphs, a.k.a. vertex embedding, is an active field of research. Vertex embedding enables the application of relational data mining techniques to network data. Unintended use of vertex embedding unveils a novel generic method for centrality computation using neural networks. The new centrality measure, termed Embedding Centrality, proposed in this paper is defined as the dot product of a vertex and the center of mass of the graph. Simulation results confirm the validity of Embedding Centrality which correlates well with other commonly used centrality measures. Embedding Centrality can be tailored to specific applications by devising the appropriate context for vertex embedding and can facilitate further understanding of supervised and unsupervised learning methods on graph data.

Original language	English
Title of host publication	Springer Proceedings in Complexity
Editors	Sean Cornelius, Kate Coronges, Bruno Goncalves, Roberta Sinatra, Alessandro Vespignani
Publisher	Springer Science and Business Media B.V.
Pages	87-97
Number of pages	11
ISBN (Print)	9783319731971
DOIs	https://doi.org/10.1007/978-3-319-73198-8_8
State	Published - 1 Jan 2018
Event	9th International Conference on Complex Networks, CompleNet 2018 - Boston, United States Duration: 5 Mar 2018 → 8 Mar 2018

Publication series

Name	Springer Proceedings in Complexity
Volume	0
ISSN (Print)	2213-8684
ISSN (Electronic)	2213-8692

Conference

Conference	9th International Conference on Complex Networks, CompleNet 2018
Country/Territory	United States
City	Boston
Period	5/03/18 → 8/03/18

ASJC Scopus subject areas

Applied Mathematics
Modeling and Simulation
Computer Science Applications

Access to Document

10.1007/978-3-319-73198-8_8

Cite this

Puzis, R., Sofer, Z., Cohen, D., & Hugi, M. (2018). Embedding-centrality: Generic centrality computation using neural networks. In S. Cornelius, K. Coronges, B. Goncalves, R. Sinatra, & A. Vespignani (Eds.), Springer Proceedings in Complexity (pp. 87-97). (Springer Proceedings in Complexity; Vol. 0). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-319-73198-8_8

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title = "Embedding-centrality: Generic centrality computation using neural networks",

abstract = "Deriving vector representations of vertices in graphs, a.k.a. vertex embedding, is an active field of research. Vertex embedding enables the application of relational data mining techniques to network data. Unintended use of vertex embedding unveils a novel generic method for centrality computation using neural networks. The new centrality measure, termed Embedding Centrality, proposed in this paper is defined as the dot product of a vertex and the center of mass of the graph. Simulation results confirm the validity of Embedding Centrality which correlates well with other commonly used centrality measures. Embedding Centrality can be tailored to specific applications by devising the appropriate context for vertex embedding and can facilitate further understanding of supervised and unsupervised learning methods on graph data.",

author = "Rami Puzis and Zion Sofer and Dvir Cohen and Matan Hugi",

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Puzis, R, Sofer, Z, Cohen, D & Hugi, M 2018, Embedding-centrality: Generic centrality computation using neural networks. in S Cornelius, K Coronges, B Goncalves, R Sinatra & A Vespignani (eds), Springer Proceedings in Complexity. Springer Proceedings in Complexity, vol. 0, Springer Science and Business Media B.V., pp. 87-97, 9th International Conference on Complex Networks, CompleNet 2018, Boston, United States, 5/03/18. https://doi.org/10.1007/978-3-319-73198-8_8

Embedding-centrality: Generic centrality computation using neural networks. / Puzis, Rami; Sofer, Zion; Cohen, Dvir et al.
Springer Proceedings in Complexity. ed. / Sean Cornelius; Kate Coronges; Bruno Goncalves; Roberta Sinatra; Alessandro Vespignani. Springer Science and Business Media B.V., 2018. p. 87-97 (Springer Proceedings in Complexity; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Embedding-centrality

T2 - 9th International Conference on Complex Networks, CompleNet 2018

AU - Puzis, Rami

AU - Sofer, Zion

AU - Cohen, Dvir

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N1 - Publisher Copyright: © Springer International Publishing AG 2018.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Deriving vector representations of vertices in graphs, a.k.a. vertex embedding, is an active field of research. Vertex embedding enables the application of relational data mining techniques to network data. Unintended use of vertex embedding unveils a novel generic method for centrality computation using neural networks. The new centrality measure, termed Embedding Centrality, proposed in this paper is defined as the dot product of a vertex and the center of mass of the graph. Simulation results confirm the validity of Embedding Centrality which correlates well with other commonly used centrality measures. Embedding Centrality can be tailored to specific applications by devising the appropriate context for vertex embedding and can facilitate further understanding of supervised and unsupervised learning methods on graph data.

AB - Deriving vector representations of vertices in graphs, a.k.a. vertex embedding, is an active field of research. Vertex embedding enables the application of relational data mining techniques to network data. Unintended use of vertex embedding unveils a novel generic method for centrality computation using neural networks. The new centrality measure, termed Embedding Centrality, proposed in this paper is defined as the dot product of a vertex and the center of mass of the graph. Simulation results confirm the validity of Embedding Centrality which correlates well with other commonly used centrality measures. Embedding Centrality can be tailored to specific applications by devising the appropriate context for vertex embedding and can facilitate further understanding of supervised and unsupervised learning methods on graph data.

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Embedding-centrality: Generic centrality computation using neural networks

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