  Anomaly detection in attributed networks has received a considerable
attention in recent years due to its applications in a wide range of domains
such as finance, network security, and medicine. Traditional approaches cannot
be adopted on attributed networks' settings to solve the problem of anomaly
detection. The main limitation of such approaches is that they inherently
ignore the relational information between data features. With a rapid explosion
in deep learning- and graph neural networks-based techniques, spotting rare
objects on attributed networks has significantly stepped forward owing to the
potentials of deep techniques in extracting complex relationships. In this
paper, we propose a new architecture on anomaly detection. The main goal of
designing such an architecture is to utilize multi-task learning which would
enhance the detection performance. Multi-task learning-based anomaly detection
is still in its infancy and only a few studies in the existing literature have
catered to the same. We incorporate both community detection and multi-view
representation learning techniques for extracting distinct and complementary
information from attributed networks and subsequently fuse the captured
information for achieving a better detection result. The mutual collaboration
between two main components employed in this architecture, i.e.,
community-specific learning and multi-view representation learning, exhibits a
promising solution to reach more effective results.
