AbstractNetwork theory provides a framework to model interacting systems from many different fields. The recent increase in the availability of empirical data has allowed the research community to take into account more realistic characteristics of the networks. In particular, the details of the timing of the interactions have highlighted the non-Markovian nature of the agents in many systems. This work lies in the context of stochastic processes on temporal network and is twofold.
The first part of this work aims at studying the impact of non-Markovian activities on Random Walkstaking place on temporal networks. We show that memory in the trajectory of the random walker naturally emerges due to bursty behaviours and the presence of short cycles.
The second part of this work aims at designing efficient spreading strategies on temporal networks. In particular, we show that the temporal sequence of a cascade of retweets may provide an insight about the way it spread on the network. We exploit these findings by developing the SmartInf algorithm that provides a list of users to target simultaneously, in order to maximize the final share of a message. Importantly, SmartInf relies on temporal patterns of cascades and on local structural information, in opposition to standard methods that rely on the typically costly global structure.
|Date of Award||2019|
|Supervisor||Renaud Lambiotte (Supervisor), Jean Charles Delvenne (Co-Supervisor), Anne Lemaitre (President), Julien Hendrickx (Jury), Luis E. C. Rocha (Jury) & Timoteo Carletti (Jury)|
- Temporal networks
- social networks
- random walks
- information diffusion