Dipartimento di Informatica

Abstract: In this talk we will give a high-level overview of symmetry reductions based on linear graph-based invariants. These reduce models to smaller ones while preserving properties of interest, allowing thus to reduce simulation and computation times. Network embedding, for instance, allows to project networks onto lower-dimensional spaces while preserving similarities among nodes. Likewise, differential equivalences reduce chemical reaction networks to smaller ones while preserving biological measurements. The applicability will be demonstrated on benchmarks from network science and systems biology.