Cited By
View all- Gong STian CYin QYu WZhang YGeng LYu SYu GZhou J(2021)Automating incremental graph processing with flexible memoizationProceedings of the VLDB Endowment10.14778/3461535.346155014:9(1613-1625)Online publication date: 22-Oct-2021
Addgraph: anomaly detection in dynamic graph using attention-based temporal GCN
Authors: 
Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, Jun GaoAuthors Info & Claims
Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, Jun GaoAuthors Info & ClaimsPages 4419 - 4425
Abstract
Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e.g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data. It is better to learn the anomaly patterns by considering all possible hints including the structural, content and temporal features, rather than utilizing heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture both long-term patterns and the short-term patterns in dynamic graphs. In order to cope with insufficient explicit labelled data, we employ a selective negative sampling and margin loss in training of AddGraph in a semi-supervised fashion. We conduct extensive experiments on real-world datasets, and illustrate that AddGraph can outperform the state-of-the-art competitors in anomaly detection significantly.
References
[1]
Charu C Aggarwal, Yuchen Zhao, and S Yu Philip. Outlier detection in graph streams. In 2011 IEEE 27th International Conference on Data Engineering, pages 399-409. IEEE, 2011.
[2]
Leman Akoglu, Hanghang Tong, and Danai Koutra. Graph based anomaly detection and description: a survey. Data mining and knowledge discovery, 29(3):626-688, 2015.
[3]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pages 2787-2795, 2013.
[4]
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
[5]
Qiang Cui, Shu Wu, Yan Huang, and Liang Wang. A hierarchical contextual attention-based gru network for sequential recommendation. arXiv preprint arXiv:1711.05114, 2017.
[6]
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems, pages 3844-3852, 2016.
[7]
Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra. Spotlight: Detecting anomalies in streaming graphs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1378-1386. ACM, 2018.
[8]
Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864. ACM, 2016.
[9]
Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. Fraudar: Bounding graph fraud in the face of camouflage. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 895-904. ACM, 2016.
[10]
Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR), 2017.
[11]
Jun Liu, Gang Wang, Ping Hu, Ling-Yu Duan, and Alex C Kot. Global context-aware attention lstm networks for 3d action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1647-1656, 2017.
[12]
Ryan McConville, Weiru Liu, and Paul Miller. Vertex clustering of augmented graph streams. In Proceedings of the 2015 SIAM International Conference on Data Mining, pages 109-117. SIAM, 2015.
[13]
Stephen Ranshous, Shitian Shen, Danai Koutra, Steve Harenberg, Christos Faloutsos, and Nagiza F Samatova. Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3):223-247, 2015.
[14]
Stephen Ranshous, Steve Harenberg, Kshitij Sharma, and Nagiza F Samatova. A scalable approach for outlier detection in edge streams using sketch-based approximations. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 189-197. SIAM, 2016.
[15]
Kijung Shin, Bryan Hooi, and Christos Faloutsos. M-zoom: Fast dense-block detection in tensors with quality guarantees. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 264-280. Springer, 2016.
[16]
Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos. D-cube: Dense-block detection in terabyte-scale tensors. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 681-689. ACM, 2017.
[17]
Kumar Sricharan and Kamalika Das. Localizing anomalous changes in time-evolving graphs. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pages 1347- 1358. ACM, 2014.
[18]
Jimeng Sun, Dacheng Tao, and Christos Faloutsos. Beyond streams and graphs: dynamic tensor analysis. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 374-383. ACM, 2006.
[19]
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Largescale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, pages 1067-1077. International World Wide Web Conferences Steering Committee, 2015.
[20]
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph embedding by translating on hyperplanes. In Twenty-Eighth AAAI conference on artificial intelligence, 2014.
[21]
Wenchao Yu, Wei Cheng, Charu C Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2672-2681. ACM, 2018.
[22]
Yuchen Zhao and Philip S Yu. On graph stream clustering with side information. In Proceedings of the 2013 SIAM International Conference on Data Mining, pages 139-150. SIAM, 2013.
Recommendations
Autoencoding Binary Classifiers for Supervised Anomaly Detection
PRICAI 2019: Trends in Artificial IntelligenceAbstractWe propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects ...
Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation
AbstractRecent surface anomaly detection methods rely on pretrained backbone networks for efficient anomaly detection. On standard RGB anomaly detection benchmarks these methods achieve excellent results but fail on 3D anomaly detection due to a lack of ...
Highlights- A novel 3D anomaly simulation process is proposed.
- Simulation enables the training of discriminative methods for 3D anomaly detection.
- The 3DRÆM anomaly detection method demonstrates the simulation process efficiency.
- 3DRÆM ...
Two-stage anomaly detection algorithm via dynamic community evolution in temporal graph
AbstractDetecting anomalies from a massive amount of user behavioral data is often liken to finding a needle in a haystack. While tremendous efforts have been devoted to anomaly detection from temporal graphs, existing studies rarely consider community ...
Comments
Information & Contributors
Information
Published In
Sponsors
- Sony: Sony Corporation
- Huawei Technologies Co. Ltd.: Huawei Technologies Co. Ltd.
- Baidu Research: Baidu Research
- The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)
- Lenovo: Lenovo
Publisher
AAAI Press
Publication History
Published: 10 August 2019
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Jan 2025
Other Metrics
Citations
Cited By
View all- Gong STian CYin QYu WZhang YGeng LYu SYu GZhou J(2021)Automating incremental graph processing with flexible memoizationProceedings of the VLDB Endowment10.14778/3461535.346155014:9(1613-1625)Online publication date: 22-Oct-2021