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Efficient influence maximization under network uncertainty

Efficient influence maximization under network uncertainty
Efficient influence maximization under network uncertainty
We consider the influence maximization (IM) problem in a partially visible social network. The goal is to design a decision-making framework for an autonomous agent to select a limited set of influential seed nodes to spread a message as widely as possible across the network. We consider the realistic case where only a partial section of the network is visible to the agent, while the rest is one of a finite set of known structures, each with a given realization probability. We show that solving the IM problem in this setting is NP-hard, and we provide analytical guarantees for the performance of a novel computationally-efficient seed-selection approximation algorithm for the agent. In empirical experiments on real-world social networks, we demonstrate the efficiency of our scheme and show that it outperforms state-of-the-art approaches that do not model the uncertainty.
365-371
IEEE
Eshghi, Soheil
f2d9c708-add0-4840-b4f8-4f8ce8a5b2dd
Maghsudi, Setareh
537eed0a-7f24-4800-8491-465ee31b67be
Restocchi, Valerio
39654f4e-2a84-4c78-a853-081704568415
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Tassiulas, Leandros
7457d150-1cdc-48de-9aff-308549167f33
Eshghi, Soheil
f2d9c708-add0-4840-b4f8-4f8ce8a5b2dd
Maghsudi, Setareh
537eed0a-7f24-4800-8491-465ee31b67be
Restocchi, Valerio
39654f4e-2a84-4c78-a853-081704568415
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Tassiulas, Leandros
7457d150-1cdc-48de-9aff-308549167f33

Eshghi, Soheil, Maghsudi, Setareh, Restocchi, Valerio, Stein, Sebastian and Tassiulas, Leandros (2019) Efficient influence maximization under network uncertainty. In First International INFOCOM Workshop on Communications and Networking Aspects of Online Social Networks (IEEE CAOS 2019). IEEE. pp. 365-371 . (doi:10.1109/INFCOMW.2019.8845088).

Record type: Conference or Workshop Item (Paper)

Abstract

We consider the influence maximization (IM) problem in a partially visible social network. The goal is to design a decision-making framework for an autonomous agent to select a limited set of influential seed nodes to spread a message as widely as possible across the network. We consider the realistic case where only a partial section of the network is visible to the agent, while the rest is one of a finite set of known structures, each with a given realization probability. We show that solving the IM problem in this setting is NP-hard, and we provide analytical guarantees for the performance of a novel computationally-efficient seed-selection approximation algorithm for the agent. In empirical experiments on real-world social networks, we demonstrate the efficiency of our scheme and show that it outperforms state-of-the-art approaches that do not model the uncertainty.

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Efficient Influence Maximization Under Network Uncertainty final version SE - Accepted Manuscript
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Accepted/In Press date: 22 February 2019
e-pub ahead of print date: 29 April 2019
Published date: 23 September 2019
Venue - Dates: First International INFOCOM Workshop on Communications and Networking Aspects of Online Social Networks (IEEE CAOS 2019), France, 2019-04-29 - 2019-05-02

Identifiers

Local EPrints ID: 430485
URI: http://eprints.soton.ac.uk/id/eprint/430485
PURE UUID: aa8492e4-4f31-44cb-afa0-4abd4907168b

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Date deposited: 02 May 2019 16:30
Last modified: 06 Oct 2020 21:09

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