Thesis topics

Master’s Thesis Projects

Information Spreading in Networks

Motivation: Modeling the stochastic dynamics of diffusion processes in complex networks has many applications, e.g. modeling, understanding, predicting, and controlling the outbreak of epidemics, the spread of rumors and memes, and the interactions of interconnected neurons.
We offer several topics in this area, for instance:

• Efficient stochastic simulation of spreading processes
• Developing vaccination strategies for epidemics on large networks
• Inferring the underlying network structure given time-series data
• Deriving differential equations describing the mean behavior of the stochastic dynamics, taking community-structure into account

Challenges: derivation of the corresponding equations; efficient implementation

Prerequisites: background in probability theory and programming

Co-Supervisor: Gerrit Großmann