Influence maximisation beyond organisational boundaries
Influence maximisation beyond organisational boundaries
We consider the problem of choosing influential members within a social network, in order to disseminate a message as widely as possible. While this so-called problem of influence maximisation has been widely studied, little work considers partially observable networks, where only part of a network is visible to the decision maker. Yet, this is critical in many applications, where an organisation needs to distribute its message far beyond its boundaries and beyond its usual sphere of influence. In this paper, we show that existing algorithms are not sufficient to handle such scenarios. To address this, we propose a set of novel adaptive algorithms that perform well in partially observable settings, achieving an up to 18% improvement on the non-adaptive state of the art.
Stein, Sebastian
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Eshghi, Soheil
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Maghsudi, Setareh
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Tassiulas, Leandros
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Bellamy, Rachel K.E.
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Jennings, Nicholas
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6 August 2017
Stein, Sebastian
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Eshghi, Soheil
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Maghsudi, Setareh
537eed0a-7f24-4800-8491-465ee31b67be
Tassiulas, Leandros
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Bellamy, Rachel K.E.
409fdbcd-af26-481b-9ef1-052f863bce56
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stein, Sebastian, Eshghi, Soheil, Maghsudi, Setareh, Tassiulas, Leandros, Bellamy, Rachel K.E. and Jennings, Nicholas
(2017)
Influence maximisation beyond organisational boundaries.
In 3rd IEEE Smart World Congress (SmartWorld 2017).
IEEE. 6 pp.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We consider the problem of choosing influential members within a social network, in order to disseminate a message as widely as possible. While this so-called problem of influence maximisation has been widely studied, little work considers partially observable networks, where only part of a network is visible to the decision maker. Yet, this is critical in many applications, where an organisation needs to distribute its message far beyond its boundaries and beyond its usual sphere of influence. In this paper, we show that existing algorithms are not sufficient to handle such scenarios. To address this, we propose a set of novel adaptive algorithms that perform well in partially observable settings, achieving an up to 18% improvement on the non-adaptive state of the art.
Text
IEEE- SWC-DAIS-19_0
- Accepted Manuscript
More information
Accepted/In Press date: 7 April 2017
e-pub ahead of print date: 6 August 2017
Published date: 6 August 2017
Venue - Dates:
DAIS ITA Workshop on Distributed Analytics Infrastructure and Algorithms for Multi-Organization Federations, San Francisco, United States, 2017-08-06 - 2017-08-07
Identifiers
Local EPrints ID: 415162
URI: https://eprints.soton.ac.uk/id/eprint/415162
PURE UUID: 11e0da0e-c17f-4aa4-becd-514e83886761
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Date deposited: 02 Nov 2017 17:30
Last modified: 06 Oct 2018 04:04
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Contributors
Author:
Sebastian Stein
Author:
Soheil Eshghi
Author:
Setareh Maghsudi
Author:
Leandros Tassiulas
Author:
Rachel K.E. Bellamy
Author:
Nicholas Jennings
University divisions
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