Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies
Résumé
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.
Mots clés
- Scalability Reproducibility. We provide
- Social Networks
- Anomaly Detection Link Streams Edge Streams Temporal Graphs Dynamic Graphs Security Social Networks Financial Transactions Scalability Reproducibility. We provide
- Anomaly Detection
- Link Streams
- Edge Streams
- Temporal Graphs
- Dynamic Graphs
- Security
- Financial Transactions
- Scalability
- Reproducibility
