  Graph anomaly detection on attributed networks has become a prevalent
research topic due to its broad applications in many influential domains. In
real-world scenarios, nodes and edges in attributed networks usually display
distinct heterogeneity, i.e. attributes of different types of nodes show great
variety, different types of relations represent diverse meanings. Anomalies
usually perform differently from the majority in various perspectives of
heterogeneity in these networks. However, existing graph anomaly detection
approaches do not leverage heterogeneity in attributed networks, which is
highly related to anomaly detection. In light of this problem, we propose
AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach
based on the encoder-decoder framework. Specifically, for the encoder, we
design three levels of attention, i.e. attribute level, node type level, and
edge level attentions to capture the heterogeneity of network structure, node
properties and information of a single node, respectively. In the decoder, we
exploit structure, attribute, and node type reconstruction terms to obtain an
anomaly score for each node. Extensive experiments show the superiority of
AHEAD on several real-world heterogeneous information networks compared with
the state-of-arts in the unsupervised setting. Further experiments verify the
effectiveness and robustness of our triple attention, model backbone, and
decoder in general.
