Real-world networks have a complex topology with many interconnected elements often organized into communities. Identifying these communities helps reveal the system’s organizational and functional structure. However, network data can be noisy, with incomplete link observations, making it difficult to detect significant community structures as missing data weakens the evidence for specific solutions. Recent research shows that flow-based community detection methods can highlight spurious communities in sparse networks with incomplete link observations. To address this issue, these methods require regularization. In this talk, I will show how a Bayesian approach can be used to regularize flows in networks, reducing overfitting in the flow-based community detection method known as the map equation.

We establish general conditions for stochastic evolution equations with locally monotone drift and degenerate additive Lévy noise in variational formulation resulting in the existence of a unique invariant probability measure for the associated ergodic Markovian Feller semigroup. We prove improved moment estimates for the solutions and the e-property of the semigroup. Examples include the stochastic incompressible 2D Navier-Stokes equations, shear thickening stochastic power-law fluid equations, the stochastic heat equation, as well as, stochastic semilinear equations such as the 1D stochastic Burgers equation.

Joint work with Gerardo Barrera (IST Lisboa), https://arxiv.org/abs/2412.01381

For some values of degrees d=(d_1,...,d_c), we construct a compactification of a Hilbert scheme of complete intersections of type d. We present both a quotient and a direct construction. Then we work towards the construction of a quasiprojective coarse moduli space of smooth complete intersections via Geometric Invariant Theory.