TgeT is a project that combines evolutionary single and multi-objective optimization algorithms, social network analysis and agent-based modeling to detect influential consumers and use them in viral marketing campaigns to maximize product adoption.
TgeT is a research project developed to carry out several research tasks during the course of my doctoral thesis. Mainly, the software makes use of:
- An agent-based model focused on consumption adoption proposed by Marco Jannsen and Wagner Jagger in [1] and [2]. This model was improved through the addition of brand awareness and word-of-mouth information exchange processes to make the model more realistic in this paper.
- A social network framework for representing connections and relationships between consumer agents. This module uses GraphStream as social network API.
- Evolutionary single and multi-objective algorithms in charge of identifying the best set of influential nodes to use during viral marketing campaings for maximizing consumption adoption for a custom brand. The aforementioned modules were used for research and the findings were published in this paper.
This software is distributed under Creative Commons license.