ArticlePloS one2016
Learning from Bees: An Approach for Influence Maximization on Viral Campaigns.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep recurrent Gaussian Nesterovs recommendation using multi-agent in social networks.Evolving systems · 2022Article
- Modeling information diffusion in online social networks using a modified forest-fire model.Journal of intelligent information systems · 2021Article
- Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks.PloS one · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.
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Registered trials
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