Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies
This work addresses anomaly detection for researchers and practitioners by demonstrating that existing complex methods are unnecessary for random anomalies, suggesting a shift to more complex anomaly types.
The paper tackled the problem of detecting random anomalies in link streams, showing that simple graph features and classical learning techniques achieve extremely high detection performance with low computational cost and interpretability.
Detecting anomalies in link streams that represent various kinds of interactions is an important research topic with crucial applications. Because of the lack of ground truth data, proposed methods are mostly evaluated through their ability to detect randomly injected links. In contrast with most proposed methods, that rely on complex approaches raising computational and/or interpretability issues, we show here that trivial graph features and classical learning techniques are sufficient to detect such anomalies extremely well. This basic approach has very low computational costs and it leads to easily interpretable results. It also has many other desirable properties that we study through an extensive set of experiments. We conclude that detection methods should now target more complex kinds of anomalies.