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DC Field | Value | Language |
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dc.contributor.author | Kasthurirathna, D | - |
dc.contributor.author | Harre, M | - |
dc.contributor.author | Piraveenan, M | - |
dc.date.accessioned | 2022-02-07T10:19:40Z | - |
dc.date.available | 2022-02-07T10:19:40Z | - |
dc.date.issued | 2016-12 | - |
dc.identifier.citation | Kasthurirathna, D., Harrè, M. & Piraveenan, M. Optimising influence in social networks using bounded rationality models. Soc. Netw. Anal. Min. 6, 54 (2016). https://doi.org/10.1007/s13278-016-0367-4 | en_US |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/1004 | - |
dc.description.abstract | Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social networks suggests that players make non-optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being ‘rational’, we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. We hypothesise that bounded rationality of following a seed or adopting the strategy of a seed is negatively proportional to the distance from that node, and it follows that closeness centrality is the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying pay-offs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds, and the closeness centrality of the network provide the maximum negative influence. Further, we extend this model to a strategic decision-making scenario where each seed operates a strategy in a strategic game. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Vienna | en_US |
dc.relation.ispartofseries | Social Network Analysis and Mining;Vol 6 Issue 1 Pages 1-14 | - |
dc.subject | Social influence | en_US |
dc.subject | Game theory | en_US |
dc.subject | Bounded rationality | en_US |
dc.title | Optimising influence in social networks using bounded rationality models | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1007/s13278-016-0367-4 | en_US |
Appears in Collections: | Research Papers - Dept of Computer Science and Software Engineering Research Papers - IEEE Research Papers - SLIIT Staff Publications |
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Kasthurirathna2016_Article_OptimisingInfluenceInSocialNet.pdf | 730.43 kB | Adobe PDF | View/Open |
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