The dynamics of social networks, namely the way connections are created and strengthened between individuals, is a very complicated process with many factors interfering in the emergence and evolution of social ties. Current research tries to better explain the high complexity of such network structures, by proposing corresponding algorithmic models for the evolution of topologies and influence propagation. Nevertheless, state of the art models have multiple limitations, as they typically make simplifying assumptions about opinion interaction mechanisms with fixed thresholds, non-dynamic topologies, or pervasive opinion sources.

This project comes to push the boundaries of scientific understanding forward, on several levels, by placing novel and existing pieces of the puzzle together, in terms of better predicting the spread of opinion over large social temporal networks. The main objectives of this project are:

1. to define an original temporal agent-based interaction model (with a dynamic time-aware threshold);

2. to explore and define, through mathematical modeling and computer simulation, novel trade-off strategies for improving opinion diffusion coverage, while maintaining a minimal cost of operation for engaged spreader agents;

3. to improve the prediction accuracy of opinion distribution by integrating the temporal attenuation paradigm, with direct applicability in electoral poll forecasting;

4. to combine the obtained interaction model, with diffusion strategies, and temporal poll prediction into a simulator application for defining a robust opinion poll prediction framework.

The motivation of these research goals is supported by the social and economic impact potential of the project. Namely, inferring the underlying dynamics of social interaction is of outstanding present interest, since it has direct applicability in viral marketing, political science, and even epidemiology, for predicting the spread of a commercial, a rumor, or a virus.