Visualization of Diffusion Processes
In social networks, individuals' decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products, promote opinions, and where to run political campaigns.
In this project, we focus on diffusion projects happening over networks. In this context, a subset of individuals of a population acts as "seeds" that start the diffusion process: depending on their connections to their neighbors, then, they will or will not influence their neighbors.
As visualization of these processes, also applied to known algorithmical problems such as the "influence maximization" problem, is still an under-investigated topic, in this project the candidate shuold investigate the current state of the art on the field to identify directions for further research. Then, a visual analytics solution can be designed to advance the field in one of those identified gaps in literature.
- Arleo, Alessio, et al. "Influence Maximization With Visual Analytics." IEEE Transactions on Visualization and Computer Graphics 28.10 (2022): 3428-3440.
- Kempe, David, Jon Kleinberg, and Éva Tardos. "Maximizing the spread of influence through a social network." Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 2003.
- Rossetti, Giulio, et al. "NDlib: a python library to model and analyze diffusion processes over complex networks." International Journal of Data Science and Analytics 5.1 (2018): 61-79.
- Li, Yuchen, et al. "Influence maximization on social graphs: A survey." IEEE Transactions on Knowledge and Data Engineering 30.10 (2018): 1852-1872.