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Heuristic algorithms for influence maximization in partially observable social networks

Heuristic algorithms for influence maximization in partially observable social networks
Heuristic algorithms for influence maximization in partially observable social networks
We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible.
This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this paper, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network. We show that these algorithmsoutperform the state of the art by up to 38% in networks with partial observability.
1613-0073
20-32
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Eshghi, Soheil
f2d9c708-add0-4840-b4f8-4f8ce8a5b2dd
Maghsudi, Setareh
537eed0a-7f24-4800-8491-465ee31b67be
Tassiulas, Leandros
9258a405-376c-452d-accc-4dc8dbedc390
Bellamy, Rachel K. E.
409fdbcd-af26-481b-9ef1-052f863bce56
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Eshghi, Soheil
f2d9c708-add0-4840-b4f8-4f8ce8a5b2dd
Maghsudi, Setareh
537eed0a-7f24-4800-8491-465ee31b67be
Tassiulas, Leandros
9258a405-376c-452d-accc-4dc8dbedc390
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) Heuristic algorithms for influence maximization in partially observable social networks. In Proceedings of the 3rd International Workshop on Social Influence Analysis (SocInf 2017): in conjunction with the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, August 19, 2017. vol. 1893, pp. 20-32.

Record type: Conference or Workshop Item (Paper)

Abstract

We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible.
This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this paper, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network. We show that these algorithmsoutperform the state of the art by up to 38% in networks with partial observability.

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More information

Accepted/In Press date: 22 July 2017
Published date: 16 August 2017
Venue - Dates: 3rd International Workshop on Social Influence Analysis, Melborne, Australia, 2017-08-19 - 2017-08-25

Identifiers

Local EPrints ID: 414703
URI: https://eprints.soton.ac.uk/id/eprint/414703
ISSN: 1613-0073
PURE UUID: 21cc1160-136f-47af-a985-d15677d17f6c

Catalogue record

Date deposited: 06 Oct 2017 16:31
Last modified: 07 Aug 2018 16:33

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