  Accurate and fast simulation of particle physics processes is crucial for the
high-energy physics community. Simulating particle interactions with detectors
is both time consuming and computationally expensive. With the proton-proton
collision energy of 13 TeV, the Large Hadron Collider is uniquely positioned to
detect and measure the rare phenomena that can shape our knowledge of new
interactions. The High-Luminosity Large Hadron Collider (HL-LHC) upgrade will
put a significant strain on the computing infrastructure due to increased event
rate and levels of pile-up. Simulation of high-energy physics collisions needs
to be significantly faster without sacrificing the physics accuracy. Machine
learning approaches can offer faster solutions, while maintaining a high level
of fidelity. We discuss a graph generative model that provides effective
reconstruction of LHC events, paving the way for full detector level fast
simulation for HL-LHC.
