<abstract xmlns="http://www.w3.org/1999/xhtml"><p>One of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.</p></abstract>

One of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

Efficiency centrality in time-varying graphs

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Efficiency centrality in time-varying graphs"}

One of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is...

Efficiency centrality in time-varying graphs

Published Online: Jul 16, 2020

Page range: 5 - 21

Received: Jan 19, 2020

Accepted: Feb 18, 2020

DOI: https://doi.org/10.2478/ausi-2020-0001

Keywords
efficiency centrality, time-varying graphs, time-constrained coverage

© 2020 Péter Marjai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Efficiency centrality in time-varying graphs

Published Online: Jul 16, 2020

Page range: 5 - 21

Received: Jan 19, 2020

Accepted: Feb 18, 2020

DOI: https://doi.org/10.2478/ausi-2020-0001

Keywordsefficiency centrality, time-varying graphs, time-constrained coverage

© 2020 Péter Marjai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
efficiency centrality, time-varying graphs, time-constrained coverage