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.
Highlights
Social networking platforms, such as Facebook and Twitter, have been extensively used for socio-political movements apart from viral marketing campaigns in the recent past as an increasing number of people spend more time online
Kiss of Love protest is a non-violent protest against moral policing which started in Kerala and later spread to other parts of India
The quick diffusion of socio-political campaigns through online micro-blogging platforms such as Twitter is extremely dependent on identification seed nodes
Summary
Social networking platforms, such as Facebook and Twitter, have been extensively used for socio-political movements apart from viral marketing campaigns in the recent past as an increasing number of people spend more time online. In this scenario, it has become a challenge for the campaigners to diffuse the information quickly across the network. The influence maximisation problem originally introduced in the context of viral marketing is NP-hard for obtaining an optimal subset of users who can maximise the information diffusion [1,2,3] It has resulted in an abundant development of approximate algorithms to identify prominent actors or the so-called super spreaders [4,5,6]. We propose a bio-inspired approach to select nodes in the initial set which can lead to a more rapid information propagation process and to simulate the diffusion by identifying retweet process of Twitter campaigns with the waggle dance of a bee colony
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