A Viewpoint on Multi-Dimensional Centrality
Dr. Versavia-Maria Ancusa, Lect Engineering, Department of Computer and Software Engineering, Automation and Computer Politehnica University, Timisoara Romania.
Manuscript received on 17 August 2015 | Revised Manuscript received on 25 August 2015 | Manuscript Published on 30 August 2015 | PP: 52-54 | Volume-5 Issue-3, August 2015 | Retrieval Number: C2178085315/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Complex networks are a becoming a more and more used tool in order to represent real-life systems. Subject to inherent interdisciplinary constraints, the creation of a realist model and its interpretation are more often than not challenging. One of the most used factors in network analysis and interpretation is centrality, which measures the influence of a node in the network or the diffusion power of that node. While its computation is a relative simple problem in basic, uni-dimensional networks, this measure proves more difficult to define in multi-dimensional networks. As practice shows that multi-dimensional models are more accurate, the pressure to create a valid and easily computable influence measure increases. The aim of this paper is to present an overview of the methods used to achieve a multi-dimensional centrality. In the end, a novel method for computing centrality in multi-dimensional networks is proposed.
Keywords: Centrality, Complex Networks, Multi-Dimensional, Vector.
Scope of the Article: Embedded Networks