mlsanityData to 14 Oct 2026

Anomalous Edge Detection in Edge Exchangeable Social Network Models

arXiv 2109.12727 ↗First posted 27 Sep 2021cs.SI stat.MLcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 14 of its 59 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

14 of 59 references.

References · 14

  1. 2016Variational Inference: A Review for Statisticianscited by 1,472
  2. 2014Graph-based Anomaly Detection and Description: A Surveycited by 183
  3. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
  4. 2020Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and Morecited by 56
  5. 2020Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systemscited by 46
  6. 2020Exchangeability, Conformal Prediction, and Rank Testscited by 41
  7. 2019MIDAS: Microcluster-Based Detector of Anomalies in Edge Streamscited by 29
  8. 2020F-FADE: Frequency Factorization for Anomaly Detection in Edge Streamscited by 26
  9. 2013Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structurescited by 22
  10. 2014Sparse graphs using exchangeable random measurescited by 20
  11. 2017Inductive Conformal Martingales for Change-Point Detectioncited by 19
  12. 2017Conformal k-NN Anomaly Detector for Univariate Data Streamscited by 13
2 more
  1. 2016Edge-exchangeable graphs and sparsitycited by 7
  2. 2014Exchangeable Variable Modelscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 6; 2025: 5; 2026: 2.

17 papers.

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  1. 2024Trustworthy Classification through Rank-Based Conformal Prediction Setscited by 20
  2. 2023Conformal Prediction for Network-Assisted Regressioncited by 14
  3. 2024Conformal Load Prediction with Transductive Graph Autoencoderscited by 11
  4. 2024Weighted Aggregation of Conformity Scores for Classificationcited by 11
  5. 2023Conformal link prediction for false discovery rate controlcited by 9
  6. 2022Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chainscited by 8
  7. 2024Entropy Reweighted Conformal Classificationcited by 8
  8. 2024Conformalized Interval Arithmetic with Symmetric Calibrationcited by 8
  9. 2025Conditional Conformal Risk Adaptationcited by 7
  10. 2025Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regressioncited by 6
  11. 2025Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliabilitycited by 6
  12. 2025Enhanced Route Planning with Calibrated Uncertainty Setcited by 5
5 more
  1. 2024Uncertainty Quantification on Graph Learning: A Surveycited by 4
  2. 2026Fast Conformal Prediction using Conditional Interquantile Intervalscited by 4
  3. 2023Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphscited by 2
  4. 2025Residual Reweighted Conformal Prediction for Graph Neural Networkscited by 2
  5. 2026Conflict Forecasting via Conformal Prediction for Markov Processescited by 0