Multitask Active Learning for Graph Anomaly Detection
Wenjing Chang Kay Liu Kaize Ding Philip S. Yu Jianjun Yu
In the web era, graph machine learning has been widely used on ubiquitous
graph-structured data. As a pivotal component for bolstering web security and
enhancing the robustness of graph-based applications, the significance of graph
anomaly detection is continually increasing. While Graph Neural Networks (GNNs)
have demonstrated efficacy in supervised and semi-supervised graph anomaly
detection, their performance is contingent upon the availability of sufficient
ground truth labels. The labor-intensive nature of identifying anomalies from
complex graph structures poses a significant challenge in real-world
applications. Despite that, the indirect supervision signals from other tasks
(e.g., node classification) are relatively abundant. In this paper, we propose
a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE.
Firstly, by coupling node classification tasks, MITIGATE obtains the capability
to detect out-of-distribution nodes without known anomalies. Secondly, MITIGATE
quantifies the informativeness of nodes by the confidence difference across
tasks, allowing samples with conflicting predictions to provide informative yet
not excessively challenging information for subsequent training. Finally, to
enhance the likelihood of selecting representative nodes that are distant from
known patterns, MITIGATE adopts a masked aggregation mechanism for distance
measurement, considering both inherent features of nodes and current labeled
status. Empirical studies on four datasets demonstrate that MITIGATE
significantly outperforms the state-of-the-art methods for anomaly detection.
Our code is publicly available at: https://github.com/AhaChang/MITIGATE.