Anomalous Edge Detection in Edge Exchangeable Social Network Models

Rui Luo, Buddhika Nettasinghe, Vikram Krishnamurthy
Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 204:287-310, 2023.

Abstract

This paper studies detecting anomalous edges in directed graphs that model social networks. We exploit edge exchangeability as a criterion for distinguishing anomalous edges from normal edges. Then we present an anomaly detector based on conformal prediction theory; this detector has a guaranteed upper bound for false positive rate. In numerical experiments, we show that the proposed algorithm achieves superior performance to baseline methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v204-luo23a, title = {Anomalous Edge Detection in Edge Exchangeable Social Network Models}, author = {Luo, Rui and Nettasinghe, Buddhika and Krishnamurthy, Vikram}, booktitle = {Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {287--310}, year = {2023}, editor = {Papadopoulos, Harris and Nguyen, Khuong An and Boström, Henrik and Carlsson, Lars}, volume = {204}, series = {Proceedings of Machine Learning Research}, month = {13--15 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v204/luo23a/luo23a.pdf}, url = {https://proceedings.mlr.press/v204/luo23a.html}, abstract = {This paper studies detecting anomalous edges in directed graphs that model social networks. We exploit edge exchangeability as a criterion for distinguishing anomalous edges from normal edges. Then we present an anomaly detector based on conformal prediction theory; this detector has a guaranteed upper bound for false positive rate. In numerical experiments, we show that the proposed algorithm achieves superior performance to baseline methods.} }
Endnote
%0 Conference Paper %T Anomalous Edge Detection in Edge Exchangeable Social Network Models %A Rui Luo %A Buddhika Nettasinghe %A Vikram Krishnamurthy %B Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2023 %E Harris Papadopoulos %E Khuong An Nguyen %E Henrik Boström %E Lars Carlsson %F pmlr-v204-luo23a %I PMLR %P 287--310 %U https://proceedings.mlr.press/v204/luo23a.html %V 204 %X This paper studies detecting anomalous edges in directed graphs that model social networks. We exploit edge exchangeability as a criterion for distinguishing anomalous edges from normal edges. Then we present an anomaly detector based on conformal prediction theory; this detector has a guaranteed upper bound for false positive rate. In numerical experiments, we show that the proposed algorithm achieves superior performance to baseline methods.
APA
Luo, R., Nettasinghe, B. & Krishnamurthy, V.. (2023). Anomalous Edge Detection in Edge Exchangeable Social Network Models. Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 204:287-310 Available from https://proceedings.mlr.press/v204/luo23a.html.

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