Abstract
The graph-based anomaly detection task aims to identify nodes with patterns that deviate from those of the majority nodes in a large graph, where only a limited subset of nodes is annotated. However, inadequate supervised knowledge and uncertainty of anomalous structure restrict the performance of detection. In this paper, we propose PERTAD (Pseudo VERificaTion for Anomaly Detection), a novel semi-supervised learning method for detecting anomalies in partially labeled graphs. Specifically, we first propose a self-verification framework that comprises a target network (T-model) and a verification network (V-model). The framework employs pseudo-labeled graphs to transfer the knowledge learned by T-model to V-model, and then corrects T-model with the performance error between the two networks on the labeled set. Furthermore, we introduce a layer-level aggregation mechanism for node representation in deep GNNs to address the uncertainty of anomalous structures. The proposed mechanism re-aggregates neighborhood information across layers, aiming to preserve low-order neighborhood characteristics and alleviate the over-smoothing effect. Extensive experiments on real-world graph-based anomaly detection tasks demonstrate that PERTAD significantly outperforms state-of-the-art baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cascante-Bonilla, P., Tan, F., Qi, Y., Ordonez, V.: Curriculum labeling: revisiting pseudo-labeling for semi-supervised learning. In: AAAI, pp. 6912–6920 (2021)
Deng, J., Li, W., Chen, Y., Duan, L.: Unbiased mean teacher for cross-domain object detection. In: CVPR, pp. 4091–4101 (2021)
Ding, K., Li, J., Bhanushali, R., Liu, H.: Deep anomaly detection on attributed networks. In: SIAM, pp. 594–602 (2019)
Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., Yu, P.S.: Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: CIKM (2020)
Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS, pp. 1024–1034 (2017)
Han, K., Rebuffi, S.A., Ehrhardt, S., Vedaldi, A., Zisserman, A.: Automatically discovering and learning new visual categories with ranking statistics. In: ICLR (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 4700–4708 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Kumar, S., Hooi, B., Makhija, D., Kumar, M., Faloutsos, C., Subrahmanian, V.: Rev2: fraudulent user prediction in rating platforms. In: WSDM (2018)
Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: SIGKDD, pp. 1269–1278 (2019)
Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: ICML, p. 896 (2013)
Liu, M., Gao, H., Ji, S.: Towards deeper graph neural networks. In: SIGKDD, pp. 338–348 (2020)
Liu, Y., et al.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In: WWW 2021: Proceedings of the Web Conference 2021, pp. 3168–3177 (2021)
Liu, Z., Dou, Y., Yu, P.S., Deng, Y., Peng, H.: Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: SIGIR (2020)
Liu, Z., et al.: GeniePath: graph neural networks with adaptive receptive paths. In: AAAI, pp. 4424–4431 (2019)
Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., Song, L.: Heterogeneous graph neural networks for malicious account detection. In: CIKM, pp. 2077–2085 (2018)
Lokhande, V.S., Tasneeyapant, S., Venkatesh, A., Ravi, S.N., Singh, V.: Generating accurate pseudo-labels in semi-supervised learning and avoiding overconfident predictions via hermite polynomial activations. In: CVPR, pp. 11435–11443 (2020)
Pham, H., Dai, Z., Xie, Q., Le, Q.V.: Meta pseudo labels. In: CVPR (2021)
RoyChowdhury, A., et al.: Automatic adaptation of object detectors to new domains using self-training. In: CVPR, pp. 780–790 (2019)
Sohn, K., et al.: FixMatch: simplifying semi-supervised learning with consistency and confidence. In: NeurIPS, pp. 596–608 (2020)
Tang, J., Li, J., Gao, Z., Li, J.: Rethinking graph neural networks for anomaly detection. In: ICML, pp. 21076–21089 (2022)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)
Wang, D., et al.: A semi-supervised graph attentive network for financial fraud detection. In: ICDM, pp. 598–607 (2019)
Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., Chen, H.: Decoupling representation learning and classification for GNN-based anomaly detection. In: SIGIR (2021)
Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. In: NeurIPS, pp. 6256–6268 (2020)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks. In: ICLR (2019)
Zhang, X., Ge, Y., Qiao, Y., Li, H.: Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification. In: CVPR (2021)
Acknowledgment
This research is supported by a grant from the National Key Research and Development Program of China (No. 2022YFF0711801), the National Natural Science Foundation of China (Grant No. J2224012) and the CAS 145 Informatization Project CAS-WX2022GC-0301. Jianjun Yu is the corresponding author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chang, W., Yu, J., Zhou, X. (2024). PERTAD: Towards Pseudo Verification for Anomaly Detection in Partially Labeled Graphs. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_14
Download citation
DOI: https://doi.org/10.1007/978-981-97-2421-5_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2420-8
Online ISBN: 978-981-97-2421-5
eBook Packages: Computer ScienceComputer Science (R0)