Modelling and Solving the Influence Maximisation Problems in Social Networks

Research Objective:

Nowadays, online social networking sites are no longer confined to social interactions between friends and family members but are also used in a wide range of corporate operations, such as product/service marketing, corporate branding, and customer reviews. A piece of information in a social network could rapidly become so pervasive through the 'word-of-mouth' effect. Knowledge of influential individuals on actual networks is crucial for developing strategies for maximising the spread of positive items (e.g., news and innovation) and limiting the spread of negative items (e.g., disease and misinformation). Finding an optimum set of users with the most influence, known as the Influence Maximisation (IM) problem, has received great attention with many application potentials, including viral marketing, targeted advertising, rumour control, recommendation, and poll campaign. This research project aims to develop and present generic techniques and tools for managers and decision-makers to provide decision support and improved insights into solving IM problems in social networks in various real-life uncertain and dynamic circumstances. The specific objectives of this research are:

  1. To develop scalable and effective algorithms for solving the IM problems in large-size real-world networks.
  2. To propose formulations and algorithms for solving the IM problems under multiple real-life constraints.
  3. To provide a generalised framework, formulation, and algorithm for solving the multi-objective IM problem.
  4. To design a novel diffusion model considering various uncertainty factors to better reflects the real-world scenario. 

Project start date: June 2019.

Expected completion date: May 2023.



  1. Biswas, T.K., Abbasi, A. and Chakrabortty, R.K., 2021. An MCDM integrated adaptive simulated annealing approach for influence maximisation in social networks. Information Sciences, 556, pp.27-48.
  2. Biswas, T.K., Abbasi, A. and Chakrabortty, R.K., 2022. A two-stage VIKOR assisted multi-operator differential evolution approach for Influence Maximisation in social networks. Expert Systems with Applications, 192, p.116342.


  1. Biswas, T.K., Abbasi, A. and Chakrabortty, R.K., 2020, December. A Hybrid Community-based Simulated Annealing Approach for Influence Maximization in Social Networks. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1184-1188). IEEE.