  Top-squarks (stops) play a crucial role for the naturalness of supersymmetry
(SUSY). However, searching for the stops is a tough task at the LHC. To dig the
stops out of the huge LHC data, various expert-constructed kinematic variables
or cutting-edge analysis techniques have been invented. In this paper, we
propose to represent collision events as event graphs and use the message
passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we
use our method in the search of the stop pair production at the LHC, and find
that our MPNN can efficiently discriminate the signal and background events. In
comparison with other machine learning methods (e.g. DNN), MPNN can enhance the
mass reach of stop mass by several tens of GeV to over a hundred GeV.
