Influence diffusion in social networks: modelling, prediction, and control
Talk title: Influence diffusion in social networks: modelling, prediction, and control.
Speaker affiliation: Dr. Doina Bucur, University of Twente, The Netherlands.
Abstract: Information (of influence) diffuses via links in a social network, and, even assuming that the network structure is relatively static, the size of an information cascade is hard to estimate well, both over a model and over a real social network. We cover models of information diffusion, then look at methods to predict ‘important’ nodes in the network, for example single nodes which would be able to influence many others single-handedly (in other words, maximise influence), or small sets of nodes which would do so in combination. The definition of node ‘importance’ changes with the case study, and may also require the node to minimise the size of the diffusion (for example in epidemics).
Short bio: Doina works in network data science, namely on the design of algorithms for network analysis and optimisation, with methods from network science, machine learning, and evolutionary algorithms. The challenge for these algorithms is often to draw near-optimal conclusions efficiently, either from abstract models of network dynamics that are computationally expensive, or from real network data that is difficult to collect. She is an Assistant Professor in Computer Science at the University of Twente, The Netherlands.