{"type":"ScholarlyArticle","title":"Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks","authors":[{"name":"Jiaqiang Zhang","slug":"jiaqiang-zhang","given":"Jiaqiang","family":"Zhang","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Senzhang Wang","slug":"senzhang-wang","given":"Senzhang","family":"Wang","affiliation":"Central South University"},{"name":"Songcan Chen","slug":"songcan-chen","given":"Songcan","family":"Chen","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"}],"abstract":"Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.","doi":"10.24963/ijcai.2022/330","url":"https://ijcai.org/proceedings/2022/330","pdf":"https://ijcai.org/proceedings/2022/0330.pdf","markdown":"https://ijcai.org/proceedings/2022/330.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/330","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":330,"pages":{"first":2376,"last":2382},"keywords":["Machine Learning: Self-supervised Learning","Data Mining: Mining Graphs","Data Mining: Anomaly/Outlier Detection","Machine Learning: Multi-view learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987040","length_sec":79,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985183","length_sec":672,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bo et al., 2021] Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. Beyond low-frequency information in graph convolutional networks. In AAAI, 2021.","title":"Beyond low-frequency information in graph convolutional networks","authors":"Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Devlin et al., 2018] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. arXiv preprint arXiv:1810.04805, 2018.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ding et al., 2019] Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. Deep anomaly detection on attributed networks. In SDM, 2019.","title":"Deep anomaly detection on attributed networks","authors":"Ding","year":"2019","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ding et al., 2021] Kaize Ding, Jundong Li, Nitin Agarwal, and Huan Liu. Inductive anomaly detection on attributed networks. In IJCAI, 2021.","title":"Inductive anomaly detection on attributed networks","authors":"Ding","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/179"},{"position":6,"raw":"[Erfani et al., 2016] Sarah M Erfani, Sutharshan Ra- jasegarar, Shanika Karunasekera, and Christopher Leckie. High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recognition, 58:121-134, 2016.","title":"High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning","authors":"Sarah M Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, Christopher Leckie","year":"2016","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hassani and Khasahmadi, 2020] Kaveh Hassani and Amir Hosein Khasahmadi. Contrastive multi-view representation learning on graphs. In ICML, 2020.","title":"Contrastive multi-view representation learning on graphs","authors":"Kaveh Hassani, Amir Hosein, Khasahmadi","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He et al., 2021] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377, 2021.","title":"Masked autoencoders are scalable vision learners","authors":"Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jain et al., 2019] Gauri Jain, Manisha Sharma, and Basant Agarwal. Spam detection in social media using convolu- tional and long short term memory neural network. An- nals of Mathematics and Artificial Intelligence, 85(1):21- 44, 2019.","title":"Spam detection in social media using convolutional and long short term memory neural network","authors":"Gauri Jain, Manisha Sharma, Basant Agarwal","year":"2019","venue":"Annals of Mathematics and Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jin et al., 2021] Ming Jin, Yixin Liu, Yu Zheng, Lianhua Chi, Yuan-Fang Li, and Shirui Pan. Anemone: Graph anomaly detection with multi-scale contrastive learning. In CIKM, 2021.","title":"Anemone: Graph anomaly detection with multi-scale contrastive learning","authors":"Ming Jin, Yixin Liu, Yu Zheng, Lianhua Chi, Yuan-Fang Li, Shirui Pan","year":"2021","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Klicpera et al., 2019] Johannes Klicpera, Stefan Weißen- berger, and Stephan Günnemann. Diffusion improves graph learning. arXiv preprint arXiv:1911.05485, 2019.","title":"Diffusion improves graph learning","authors":"Klicpera","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2017] Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. Radar: Residual analysis for anomaly detection in attributed networks. In IJCAI, 2017.","title":"Radar: Residual analysis for anomaly detection in attributed networks","authors":"Li","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/299"},{"position":13,"raw":"[Li et al., 2019] Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, and Na Zou. Specae: Spectral autoencoder for anomaly detection in attributed networks. In CIKM, 2019.","title":"Specae: Spectral autoencoder for anomaly detection in attributed networks","authors":"Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou","year":"2019","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2021a] Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. Self- supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 2021.","title":"Selfsupervised learning: Generative or contrastive","authors":"Liu","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2021b] Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, and George Karypis. Anomaly detec- tion on attributed networks via contrastive self-supervised learning. IEEE Transactions on Neural Networks and Learning Systems, 2021.","title":"Anomaly detection on attributed networks via contrastive self-supervised learning","authors":"Liu","year":"2021","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Page et al., 1999] Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stan- ford InfoLab, 1999.","title":"The pagerank citation ranking: Bringing order to the web","authors":"Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd","year":"1999","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Peng et al., 2018] Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, and Qinghua Zheng. Anomalous: A joint mod- eling approach for anomaly detection on attributed net- works. In IJCAI, 2018.","title":"Anomalous: A joint modeling approach for anomaly detection on attributed networks","authors":"Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, Qinghua Zheng","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/488"},{"position":18,"raw":"[Peng et al., 2020] Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Jun- zhou Huang. Graph representation learning via graphical mutual information maximization. In WWW, 2020.","title":"Graph representation learning via graphical mutual information maximization","authors":"Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Perozzi and Akoglu, 2016] Bryan Perozzi and Leman Akoglu. Scalable anomaly ranking of attributed neighbor- hoods. In SDM, 2016.","title":"Scalable anomaly ranking of attributed neighborhoods","authors":"Bryan Perozzi, Leman Akoglu","year":"2016","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Shone et al., 2018] Nathan Shone, Tran Nguyen Ngoc, Vu Dinh Phai, and Qi Shi. A deep learning approach to network intrusion detection. IEEE transactions on emerging topics in computational intelligence, 2(1):41- 50, 2018.","title":"A deep learning approach to network intrusion detection","authors":"Nathan Shone, Tran Nguyen Ngoc, Dinh Vu, Qi Phai, Shi","year":"2018","venue":"IEEE transactions on emerging topics in computational intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tong et al., 2006] Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. Fast random walk with restart and its applica- tions. In ICDM, 2006.","title":"Fast random walk with restart and its applications","authors":"Hanghang Tong, Christos Faloutsos, Jia-Yu Pan","year":"2006","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Velickovic et al., 2019] Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. In ICLR, 2019.","title":"Deep graph infomax","authors":"Petar Velickovic, William Fedus, Pietro William L Hamilton, Yoshua Liò, Devon Bengio, Hjelm","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2019] Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi. A semi-supervised graph attentive network for financial fraud detection. In ICDM, 2019.","title":"A semi-supervised graph attentive network for financial fraud detection","authors":"Wang","year":"2019","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yu et al., 2018] Wenchao Yu, Wei Cheng, Charu C Aggar- wal, Kai Zhang, Haifeng Chen, and Wei Wang. Netwalk: A flexible deep embedding approach for anomaly detec- tion in dynamic networks. In KDD, 2018.","title":"Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks","authors":"Wenchao Yu, Wei Cheng, C Charu, Kai Aggarwal, Haifeng Zhang, Wei Chen, Wang","year":"2018","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zheng et al., 2021] Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T Phan, and Yi-Ping Phoebe Chen. Generative and contrastive self-supervised learning for graph anomaly detection. IEEE Transactions on Knowledge and Data En- gineering, 2021.","title":"Generative and contrastive self-supervised learning for graph anomaly detection","authors":"Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Yi-Ping Phoebe Khoa T Phan, Chen","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhu et al., 2020] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131, 2020.","title":"Deep graph contrastive representation learning","authors":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhu et al., 2021] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Graph contrastive learning with adaptive augmentation. In WWW, 2021.","title":"Graph contrastive learning with adaptive augmentation","authors":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}