  We present a novel technique for the analysis of proton-proton collision
events from the ATLAS and CMS experiments at the Large Hadron Collider. For a
given final state and choice of kinematic variables, we build a graph network
in which the individual events appear as weighted nodes, with edges between
events defined by their distance in kinematic space. We then show that it is
possible to calculate local metrics of the network that serve as event-by-event
variables for separating signal and background processes, and we evaluate these
for a number of different networks that are derived from different distance
metrics. Using a supersymmetric electroweakino and stop production as examples,
we construct prototype analyses that take account of the fact that the number
of simulated Monte Carlo events used in an LHC analysis may differ from the
number of events expected in the LHC dataset, allowing an accurate background
estimate for a particle search at the LHC to be derived. For the electroweakino
example, we show that the use of network variables outperforms both
cut-and-count analyses that use the original variables and a boosted decision
tree trained on the original variables. The stop example, deliberately chosen
to be difficult to exclude due its kinematic similarity with the top
background, demonstrates that network variables are not automatically sensitive
to BSM physics. Nevertheless, we identify local network metrics that show
promise if their robustness under certain assumptions of node-weighted networks
can be confirmed.
