  Given a complex graph database of node- and edge-attributed multi-graphs as
well as associated metadata for each graph, how can we spot the anomalous
instances? Many real-world problems can be cast as graph inference tasks where
the graph representation could capture complex relational phenomena (e.g.,
transactions among financial accounts in a journal entry), along with metadata
reflecting tabular features (e.g. approver, effective date, etc.). While
numerous anomaly detectors based on Graph Neural Networks (GNNs) have been
proposed, none are capable of directly handling directed graphs with
multi-edges and self-loops. Furthermore, the simultaneous handling of
relational and tabular features remains an unexplored area. In this work we
propose ADAMM, a novel graph neural network model that handles directed
multi-graphs, providing a unified end-to-end architecture that fuses metadata
and graph-level representation learning through an unsupervised anomaly
detection objective. Experiments on datasets from two different domains,
namely, general-ledger journal entries from different firms (accounting) as
well as human GPS trajectories from thousands of individuals (urban mobility)
validate ADAMM's generality and detection effectiveness of expert-guided and
ground-truth anomalies. Notably, ADAMM outperforms existing baselines that
handle the two data modalities (graph and metadata) separately with post hoc
synthesis efforts.
