Recent advances in modeling and analysis of large high-dimensional networks
Category: International Society for Business and Industrial Statistics (ISBIS)
Complex systems can be modeled and analyzed using network analysis where components of the system are treated as nodes. In these networks, the nodes interact over time creating edges. The edges are random variables creating dynamic networks that change over time. Examples of dynamic networks include social networks, biological networks, financial networks, and computer networks. Typically, these networks are large and sparse: the number of nodes is large and the number of edges is small comparatively. In this session, the talks will discuss methodological advances for modeling, monitoring, analyzing, and conducting inference within, large dynamic networks.
- A two-stage hold-out design for online controlled experiments on networks
- Applications of Network Representation Learning to Identify Associations of Brain Function with Political Ideology
- Influencer detection between sectors via sparse network analysis
- Monitoring large dynamic networks – who were the wolves of WallStreetBets?