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

ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach