  Anomalies represent rare observations (e.g., data records or events) that
deviate significantly from others. Over several decades, research on anomaly
mining has received increasing interests due to the implications of these
occurrences in a wide range of disciplines. Anomaly detection, which aims to
identify rare observations, is among the most vital tasks in the world, and has
shown its power in preventing detrimental events, such as financial fraud,
network intrusion, and social spam. The detection task is typically solved by
identifying outlying data points in the feature space and inherently overlooks
the relational information in real-world data. Graphs have been prevalently
used to represent the structural information, which raises the graph anomaly
detection problem - identifying anomalous graph objects (i.e., nodes, edges and
sub-graphs) in a single graph, or anomalous graphs in a database/set of graphs.
However, conventional anomaly detection techniques cannot tackle this problem
well because of the complexity of graph data. For the advent of deep learning,
graph anomaly detection with deep learning has received a growing attention
recently. In this survey, we aim to provide a systematic and comprehensive
review of the contemporary deep learning techniques for graph anomaly
detection. We compile open-sourced implementations, public datasets, and
commonly-used evaluation metrics to provide affluent resources for future
studies. More importantly, we highlight twelve extensive future research
directions according to our survey results covering unsolved and emerging
research problems and real-world applications. With this survey, our goal is to
create a "one-stop-shop" that provides a unified understanding of the problem
categories and existing approaches, publicly available hands-on resources, and
high-impact open challenges for graph anomaly detection using deep learning.
