  Detecting anomalies is important for identifying inefficiencies, errors, or
fraud in business processes. Traditional process mining approaches focus on
analyzing 'flattened', sequential, event logs based on a single case notion.
However, many real-world process executions exhibit a graph-like structure,
where events can be associated with multiple cases. Flattening event logs
requires selecting a single case identifier which creates a gap with the real
event data and artificially introduces anomalies in the event logs.
Object-centric process mining avoids these limitations by allowing events to be
related to different cases. This study proposes a novel framework for anomaly
detection in business processes that exploits graph neural networks and the
enhanced information offered by object-centric process mining. We first
reconstruct and represent the process dependencies of the object-centric event
logs as attributed graphs and then employ a graph convolutional autoencoder
architecture to detect anomalous events. Our results show that our approach
provides promising performance in detecting anomalies at the activity type and
attributes level, although it struggles to detect anomalies in the temporal
order of events.
