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Anomaly Detection in Networks via Score-Based Generative Models

Published 27 Jun 2023 in cs.LG | (2306.15324v1)

Abstract: Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it.

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References (64)
  1. Oddball: Spotting anomalies in weighted graphs. In Pacific-Asia conference on knowledge discovery and data mining, pp.  410–421. Springer, 2010.
  2. Outlier resistant unsupervised deep architectures for attributed network embedding. In Proceedings of the 13th international conference on web search and data mining, pp.  25–33, 2020.
  3. Emergence of scaling in random networks. science, 286(5439):509–512, 1999.
  4. Scale-free networks. Scientific american, 288(5):60–69, 2003.
  5. Lof: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp.  93–104, 2000.
  6. A note on over-smoothing for graph neural networks. arXiv preprint arXiv:2006.13318, 2020.
  7. Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713, 2020a.
  8. Generative adversarial attributed network anomaly detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp.  1989–1992, 2020b.
  9. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289, 2015.
  10. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780–8794, 2021.
  11. Graph neural networks as gradient flows. arXiv preprint arXiv:2206.10991, 2022.
  12. Deep anomaly detection on attributed networks. In Proceedings of the 2019 SIAM International Conference on Data Mining, pp.  594–602. SIAM, 2019.
  13. Benchmarking graph neural networks. 2020.
  14. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
  15. Freeman, L. C. Centered graphs and the structure of ego networks. Mathematical Social Sciences, 3(3):291–304, 1982.
  16. Graph drawing by force-directed placement. Software: Practice and experience, 21(11):1129–1164, 1991.
  17. Denoising diffusion models for out-of-distribution detection. arXiv preprint arXiv:2211.07740, 2022.
  18. Exploring network structure, dynamics, and function using networkx. In Varoquaux, G., Vaught, T., and Millman, J. (eds.), Proceedings of the 7th Python in Science Conference, pp.  11 – 15, Pasadena, CA USA, 2008.
  19. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  770–778, 2016.
  20. Video diffusion models. arXiv preprint arXiv:2204.03458, 2022.
  21. Dgraph: A large-scale financial dataset for graph anomaly detection. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
  22. Hunter, J. D. Matplotlib: A 2d graphics environment. Computing in science & engineering, 9(03):90–95, 2007.
  23. Score-based generative modeling of graphs via the system of stochastic differential equations. arXiv preprint arXiv:2202.02514, 2022.
  24. Variational diffusion models. Advances in neural information processing systems, 34:21696–21707, 2021.
  25. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  26. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.
  27. Krizhevsky, A. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.
  28. Rev2: Fraudulent user prediction in rating platforms. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp.  333–341, 2018.
  29. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp.  1269–1278, 2019.
  30. The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1):5–es, 2007.
  31. Radar: Residual analysis for anomaly detection in attributed networks. In IJCAI, volume 17, pp.  2152–2158, 2017.
  32. Sign and basis invariant networks for spectral graph representation learning. arXiv preprint arXiv:2202.13013, 2022.
  33. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1):1–39, 2012.
  34. Graph normalizing flows, 2019. URL https://arxiv.org/abs/1905.13177.
  35. Pygod: A python library for graph outlier detection. arXiv preprint arXiv:2204.12095, 2022a.
  36. Bond: Benchmarking unsupervised outlier node detection on static attributed graphs. arXiv preprint arXiv:2206.10071, 2022b.
  37. Diffusion denoising process for perceptron bias in out-of-distribution detection. arXiv preprint arXiv:2211.11255, 2022c.
  38. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 2021.
  39. Spam filtering with naive bayes-which naive bayes? In CEAS, volume 17, pp.  28–69. Mountain View, CA, 2006.
  40. Permutation invariant graph generation via score-based generative modeling, 2020. URL https://arxiv.org/abs/2003.00638.
  41. Automatic differentiation in pytorch. 2017.
  42. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
  43. Anomalous: A joint modeling approach for anomaly detection on attributed networks. In IJCAI, pp.  3513–3519, 2018.
  44. Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001, 2001.
  45. Scalable anomaly ranking of attributed neighborhoods. In Proceedings of the 2016 SIAM International Conference on Data Mining, pp.  207–215. SIAM, 2016.
  46. Recipe for a general, powerful, scalable graph transformer. Advances in Neural Information Processing Systems, 35:14501–14515, 2022.
  47. Diffuser: Discrete diffusion via edit-based reconstruction. arXiv preprint arXiv:2210.16886, 2022.
  48. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, pp.  4–11, 2014.
  49. Statistical selection of congruent subspaces for mining attributed graphs. In 2013 IEEE 13th international conference on data mining, pp.  647–656. IEEE, 2013.
  50. Applied stochastic differential equations, volume 10. Cambridge University Press, 2019.
  51. Graphvae: Towards generation of small graphs using variational autoencoders. In International conference on artificial neural networks, pp.  412–422. Springer, 2018.
  52. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
  53. Digress: Discrete denoising diffusion for graph generation. arXiv preprint arXiv:2209.14734, 2022.
  54. Wang, M. Y. Deep graph library: Towards efficient and scalable deep learning on graphs. In ICLR workshop on representation learning on graphs and manifolds, 2019.
  55. Decoupling representation learning and classification for gnn-based anomaly detection. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp.  1239–1248, 2021.
  56. Semi-supervised classification with graph convolutional networks. In J. International Conference on Learning Representations (ICLR 2017), 2016.
  57. Energy-based out-of-distribution detection for graph neural networks. arXiv preprint arXiv:2302.02914, 2023.
  58. Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  824–833, 2007.
  59. Contrastive attributed network anomaly detection with data augmentation. In Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part II, pp.  444–457. Springer, 2022.
  60. Graphrnn: Generating realistic graphs with deep auto-regressive models, 2018. URL https://arxiv.org/abs/1802.08773.
  61. Higher-order structure based anomaly detection on attributed networks. In 2021 IEEE International Conference on Big Data (Big Data), pp.  2691–2700. IEEE, 2021.
  62. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  586–595, 2018.
  63. Error-bounded graph anomaly loss for gnns. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp.  1873–1882, 2020.
  64. Dirichlet energy constrained learning for deep graph neural networks. Advances in Neural Information Processing Systems, 34:21834–21846, 2021.
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