  Anomaly detection is one of the most active research areas in various
critical domains, such as healthcare, fintech, and public security. However,
little attention has been paid to scholarly data, i.e., anomaly detection in a
citation network. Citation is considered as one of the most crucial metrics to
evaluate the impact of scientific research, which may be gamed in multiple
ways. Therefore, anomaly detection in citation networks is of significant
importance to identify manipulation and inflation of citations. To address this
open issue, we propose a novel deep graph learning model, namely GLAD (Graph
Learning for Anomaly Detection), to identify anomalies in citation networks.
GLAD incorporates text semantic mining to network representation learning by
adding both node attributes and link attributes via graph neural networks. It
exploits not only the relevance of citation contents but also hidden
relationships between papers. Within the GLAD framework, we propose an
algorithm called CPU (Citation PUrpose) to discover the purpose of citation
based on citation texts. The performance of GLAD is validated through a
simulated anomalous citation dataset. Experimental results demonstrate the
effectiveness of GLAD on the anomalous citation detection task.
