Authors: Zhong Li, Yuhang Wang, Matthijs van Leeuwen
Abstract: Self-supervised learning (SSL) is an emerging paradigm that exploits
supervisory signals generated from the data itself, and many recent studies
have leveraged SSL to conduct graph anomaly detection. However, we empirically
found that three important factors can substantially impact detection
performance across datasets: 1) the specific SSL strategy employed; 2) the
tuning of the strategy’s hyperparameters; and 3) the allocation of combination
weights when using multiple strategies. Most SSL-based graph anomaly detection
methods circumvent these issues by arbitrarily or selectively (i.e., guided by
label information) choosing SSL strategies, hyperparameter settings, and
combination weights. While an arbitrary choice may lead to subpar performance,
using label information in an unsupervised setting is label information leakage
and leads to severe overestimation of a method’s performance. Leakage has been
criticized as “one of the top ten data mining mistakes”, yet many recent
studies on SSL-based graph anomaly detection have been using label information
to select hyperparameters. To mitigate this issue, we propose to use an
internal evaluation strategy (with theoretical analysis) to select
hyperparameters in SSL for unsupervised anomaly detection. We perform extensive
experiments using 10 recent SSL-based graph anomaly detection algorithms on
various benchmark datasets, demonstrating both the prior issues with
hyperparameter selection and the effectiveness of our proposed strategy.
Source: http://arxiv.org/abs/2501.14694v1
