Identifying Anomalous Nodes in Multidimensional Networks
Bibliographic record
Abstract
This paper investigates the problem of the detection of anomalies in multidimensional networks, that is, networks where nodes are connected through multiple links (dimensions). Anomaly detection in monodimensional networks has been well studied and several approaches have been proposed. This problem, however, has been less investigated in the multidimensional setting. In this paper we deal with the lack of an effective approach for identifying anomalous nodes in multidimensional networks. Our contribution is two fold. First, we develop a novel scoring function that reflects the anomalousness degree of a node. Second, based on the estimated anomaly scores, we devise a probabilistic approach based on the beta mixture model to systematically discriminate between normal and anomalous nodes. A notable feature of our approach is that it performs anomaly detection in an automatic fashion without requesting human intervention to set an empirical detection threshold to detect anomalies, or by specifying the number of anomalous nodes to be selected. Furthermore, the approach that we propose exploits the topological structure of the multidimensional network as such, without considering any aggregation technique nor examining independently each dimension to identify anomalies. Experiments on synthetic as well as real networks illustrate the suitability of the proposed approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".