Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (19): 1-13.DOI: 10.3778/j.issn.1002-8331.2204-0315
• Research Hotspots and Reviews • Previous Articles Next Articles
ZHANG Yiyang, QIAN Yurong, TAO Wenbin, LENG Hongyong, LI Zichen, MA Mengnan
Online:
2022-10-01
Published:
2022-10-01
张伊扬,钱育蓉,陶文彬,冷洪勇,李自臣,马梦楠
ZHANG Yiyang, QIAN Yurong, TAO Wenbin, LENG Hongyong, LI Zichen, MA Mengnan. Survey of Attribute Graph Anomaly Detection Based on Deep Learning[J]. Computer Engineering and Applications, 2022, 58(19): 1-13.
张伊扬, 钱育蓉, 陶文彬, 冷洪勇, 李自臣, 马梦楠. 基于深度学习的属性图异常检测综述[J]. 计算机工程与应用, 2022, 58(19): 1-13.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2204-0315
[1] ZHANG M,CHEN Y.Weisfeiler-Lehman neural machine for link prediction[C]//Proceedings of the 23rd ACM SIGKDD International Conference,2017. [2] 罗世杰,吕文韬,李凡,等.融合拓扑和属性的动态网络链路预测方法[J].计算机工程与应用:1-10(2021-11-29)[2022-04-18].http://kns.cnki.net/kcms/detail/11.2127.TP.20211129. 1139.008.html. LUO S J,LYU W T,LI F,et al.Dynamic network link prediction method for fusion topology and attributes[J/OL].Computer Engineering and Applications:1-10(2021-11-29)[2022-04-18].http://kns.cnki.net/kcms/detail/11.2127.TP. 20211129.1139.008.html. [3] EBERLE W,HOLDER L.Anomaly detection in data represented as graphs[J].Intelligent Data Analysis,2007,11(6):663-689. [4] 韩涛,兰雨晴,肖利民,等.一种增量并行式动态图异常检测算法[J].北京航空航天大学学报,2018,44(1):117-124. HAN T,LAN Y Q,XIAO L M,et al.Incremental and parallel algorithm for anomaly detection in dynamic graphs[J].Journal of Beijing University of Aeronautics and Astronautics,2018,44(1):117-124 [5] GAO J,LIANG F,FAN W,et al.On community outliers and their efficient detection in information networks[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2010:813-822. [6] ABU-EL-HAIJA S,KAPOOR A,PEROZZI B,et al.N-GCN:Multi-scale graph convolution for semi-supervised node classification[J].arXiv:1802.08888,2018. [7] 吴越,王英,王鑫,等.基于超图卷积的异质网络半监督节点分类[J].计算机学报,2021,44(11):2248-2260. WU Y,WANG Y,WANG X,et al.Motif-based hypergraph convolution network for semi-supervisednode classification on heterogeneous graph[J].Chinese Journal of Computers,2021,44(11):2248-2260. [8] STANIFORD-CHEN S,CHEUNG S,CRAWFORD R,et al.GrIDS—A graph based intrusion detection system for large networks[C]//Proceedings of the 19th International Information Systems Security Conference,1996:361-370. [9] AKOGLU L,TONG H,KOUTRA D.Graph based anomaly detection and description:A survey[J].Data Mining and Knowledge Discovery,2015,29(3):626-688. [10] AKOGLU L,MCGLOHON M,FALOUTSOS C.Oddball:Spotting anomalies in weighted graphs[C]//Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining,2010:410-421. [11] BENGIO Y.Learning deep architectures for AI[M].[S.l.]:Now Publishers Inc,2009. [12] WAIKHOM L,PATGIRI R.Graph neural networks:Methods,applications,and opportunities[J].arXiv:2108. 10733,2021. [13] CROSBY T.How to detect and handle outliers[J].Technometrics,1994,16:315-316. [14] CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detection:A survey[J].ACM Computing Surveys,2009,41(3):1-58. [15] WANG K,CHEN D.Graph structure based anomaly behavior detection[C]//Proceedings of the 2nd International Conference on Computer Engineering,Information Science & Application Technology,2016:543-550. [16] JEH G,WIDOM J.Simrank:A measure of structural-context similarity[C]//Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2002:538-543. [17] CHEN H H,GILES C L.ASCOS:An asymmetric network structure context similarity measure[C]//Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining(ASONAM 2013),2013:442-449. [18] GUGGILAM S,ZAIDI S M A,CHANDOLA V,et al.Integrated clustering and anomaly detection(incad) for streaming data[C]//Proceedings of International Conference on Computational Science,2019:45-59. [19] ZHU J,WANG H.An improved K-means clustering algorithm[J].Microcomputer Information,2010,10(1):193-199. [20] HU R,AGGARWAL C C,MA S,et al.An embedding approach to anomaly detection[C]//Proceedings of IEEE 32nd International Conference on Data Engineering(ICDE),2016:385-396. [21] HELLING T J,SCHOLTES J C,TAKES F W.A community-aware approach for identifying node anomalies in complex networks[C]//Proceedings of International Conference on Complex Networks and Their Applications,2018:244-255. [22] PEROZZI B,AKOGLU L.Scalable anomaly ranking of attributed neighborhoods[J].arXiv:1601.06711,2016. [23] PEROZZI B,AKOGLU L,IGLESIAS SáNCHEZ P,et al.Focused clustering and outlier detection in large attributed graphs[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2014:1346-1355. [24] SáNCHEZ P I,MüLLER E,LAFORET F,et al.Statistical selection of congruent subspaces for mining attributed graphs[C]//Proceedings of the IEEE 13th International Conference on Data Mining,2013:647-656. [25] MüLLER E,SáNCHEZ P I,MüLLE Y,et al.Ranking outlier nodes in subspaces of attributed graphs[C]//Proceedings of the IEEE 29th International Conference on Data Engineering Workshops(ICDEW),2013:216-222. [26] LI J,DANI H,HU X,et al.Radar:Residual analysis for anomaly detection in attributed networks[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence,2017. [27] PENG Z,LUO M,LI J,et al.ANOMALOUS:A joint modeling approach for anomaly detection on attributed networks[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence,2018:3513-3519. [28] RANSHOUS S,SHEN S,KOUTRA D,et al.Anomaly detection in dynamic networks:A survey[J].Wiley Interdisciplinary Reviews:Computational Statistics,2015,7(3):223-247. [29] CHEN F,WANG Y C,WANG B,et al.Graph representation learning:A survey[J].arXiv:1909.00958,2019. [30] YANG C,LIU Z,ZHAO D,et al.Network representation learning with rich text information[C]//Proceedings of the 24th International Conference on Artificial Intelligence,2015:2111-2117. [31] XIAO H,LI J,XIA H.Accelerated attributed network embedding[C]//Proceedings of the 2017 SIAM International Conference on Data Mining,2017. [32] KIPF T N,WELLING M.Semi-supervised classification with graph convolutional networks[J].arXiv:1609.02907,2016. [33] VELI?KOVI? P,CUCURULL G,Casanova A,et al.Graph attention networks[J].arXiv:1710.10903,2017. [34] HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs[C]//Advances in Neural Information Processing Systems,2017. [35] ZHANG Z,YANG H,BU J,et al.ANRL:Attributed network representation learning via deep neural networks[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence,2018:3155-3161. [36] LI J,DANI H,HU X,et al.Attributed network embedding for learning in a dynamic environment[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management,2017:387-396. [37] LIU J,HE Z,WEI L,et al.Content to node:Self-translation network embedding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2018:1794-1802. [38] HAO X,ZHANG G,MA S.Deep learning[J].International Journal of Semantic Computing,2016,10(3):417-439. [39] LIANG J,JACOBS P,SUN J,et al.Semi-supervised embedding in attributed networks with outliers[C]//Proceedings of the 2018 SIAM International Conference on Data Mining,2018:153-161. [40] CHEN Z,LIU B,WANG M,et al.Generative adversarial attributed network anomaly detection[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management,2020:1989-1992. [41] DING K,LI J,AGARWAL N,et al.Inductive anomaly detection on attributed networks[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence,2020:1288-1294. [42] BANDYOPADHYAY S,LOKESH N,VIVEK S V,et al.Outlier resistant unsupervised deep architectures for attributed network embedding[C]//Proceedings of the Thirteenth ACM International Conference on Web Search and Data Mining,2020. [43] DING K,LI J,BHANUSHALI R,et al.Deep anomaly detection on attributed networks[C]//Proceedings of the 2019 SIAM International Conference on Data Mining,2019:594-602. [44] ZHU D,MA Y,LIU Y.Anomaly detection with deep graph autoencoders on attributed networks[C]//Proceedings of the 2020 IEEE Symposium on Computers and Communications,2020:1-6. [45] TAUBIN G.A signal processing approach to fair surface design[C]//Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques,1995:351-358. [46] LUO X,WU J,BEHESHTI A,et al.ComGA:Community-aware attributed graph anomaly detection[C]//Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining,2022:657-665. [47] PEI Y,HUANG T,IPENBURG W V,et al.ResGCN:Attention-based deep residual modeling for anomaly detection on attributed networks[J].Machine Learning,2021,111(2):519-541. [48] FAN H,ZHANG F,LI Z.AnomalyDAE:Dual autoencoder for anomaly detection on attributed networks[C]//Proceedings of the 2020 IEEE International Conference on Acoustics,Speech and Signal Processing,2020. [49] HUANG L,ZHU Y,GAO Y,et al.Hybrid-order anomaly detection on attributed networks[J].IEEE Transactions on Knowledge and Data Engineering,2021(10):1. [50] WANG D,LIN J,CUI P,et al.A semi-supervised graph attentive network for financial fraud detection[C]//Proceedings of the 2019 IEEE International Conference on Data Mining,2019. [51] CHEN L H,LI H,YANG W.AnomMAN:Detect anomaly on multi-view attributed networks[J].arXiv:2201.02822,2022. [52] DING K,SHU K,SHAN X,et al.Cross-domain graph anomaly detection[J].IEEE Transactions on Neural Networks and Learning Systems,2022,33:2406-2415. [53] WANG X,JIN B,DU Y,et al.One-class graph neural networks for anomaly detection in attributed networks[J].Neural Computing and Applications,2021,33(18):12073-12085. [54] ZHANG F,FAN H,WANG R,et al.Deep dual support vector data description for anomaly detection on attributed networks[J].International Journal of Intelligent Systems,2022,37(2):1509-1528. [55] LIU Y,LI Z,PAN S,et al.Anomaly detection on attributed networks via contrastive self-supervised learning[J].arXiv:2103.00113,2021. [56] ZHENG Y,JIN M,LIU Y,et al.Generative and contrastive self-supervised learning for graph anomaly detection[J].arXiv:2108.09896,2021. [57] ZHENG Y,JIN M,LIU Y,et al.From unsupervised to few-shot graph anomaly detection:A multi-scale contrastive learning approach[J].arXiv:2202.05525,2022. [58] YU W,CHENG W,AGGARWAL C C,et al.Netwalk:A flexible deep embedding approach for anomaly detection in dynamic networks[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2018:2672-2681. [59] HOCHREITER S,SCHMIDHUBER J.LSTM can solve hard long time lag problems[C]//Advances in Neural Information Processing Systems,1997:473-479. [60] YUAN S,ZHENG P,WU X,et al.Wikipedia vandal early detection:from user behavior to user embedding[C]//Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases,2017. [61] ZHENG P,YUAN S,WU X,et al.One-class adversarial nets for fraud detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2019:1286-1293. [62] CHO K,MERRIENBOER B V,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406. 1078,2014. [63] 郭嘉琰,李荣华,张岩,等.基于图神经网络的动态网络异常检测算法[J].软件学报,2020,31(3):748-762. GUO J Y,Li R H,ZHANG Y,et al.Graph neural network based anomaly detection in dynamic networks[J].Journal of Software,2020,31(3):748-762. [64] ZHENG L,LI Z,LI J,et al.AddGraph:Anomaly detection in dynamic graph using attention-based temporal GCN[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence,2019. [65] LI J,HAN Z,CHENG H,et al.Predicting path failure in time-evolving graphs[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2019:1279-1289. [66] CAI L,CHEN Z,LUO C,et al.Structural temporal graph neural networks for anomaly detection in dynamic graphs[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management,2021:3747-3756. [67] LIU Y,PAN S,WANG Y G,et al.Anomaly detection in dynamic graphs via transformer[J].arXiv:2106.09876,2021. [68] BREUNIG M M,KRIEGEL H P,NG R T,et al.LOF:Identifying density-based local outliers[C]//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data,2000. [69] XU X,YURUK N,FENG Z,et al.Scan:A structural clustering algorithm for networks[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2007:824-833. [70] LI Z,XIONG H,LIU Y,et al.Detecting blackhole and volcano patterns in directed networks[C]//Proceedings of the IEEE 10th International Conference on Data Mining,2010. [71] LIU Z,CHEN C,YANG X,et al.Heterogeneous graph neural networks for malicious account detection[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management,2018:2077-2085. [72] HU B,ZHANG Z,SHI C,et al.Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2019:946-953. [73] FENG S,XING L,GOGAR A,et al.Distributional footprints of deceptive product reviews[C]//Proceedings of the International AAAI Conference on Web and Social Media,2012:98-105. [74] OTT M,CHOI Y,CARDIE C,et al.Finding deceptive opinion spam by any stretch of the imagination[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies,2011. [75] AKOGLU L,CHANDY R,FALOUTSOS C.Opinion fraud detection in online reviews by network effects[C]//Proceedings of the International AAAI Conference on Web and Social Media,2013:2-11. [76] WANG G,XIE S,LIU B,et al.Review graph based online store review spammer detection[C]//Proceedings of the 11th IEEE International Conference on Data Mining,December 11-14,2011. [77] FAYYAD U M,IRANI K B.Multi-interval discretization of continuous attributes as preprocessing for classification learning[J].Internal Medicine,1995(1). [78] FALOUTSOS D H C S P C.Detecting fraudulent personalities in networks of online auctioneers[C]//Proceedings of PKDD 2006,2006:103-114. [79] PANDIT,SHASHANK,CHAU,et al.Netprobe:A fast and scalable system for fraud detection in online auction networks[C]//Proceedings of the 16th International Conference on World Wide Web,2007. [80] CORTES C,PREGIBON D,VOLINSKY C.Communities of interest[C]//Proceedings of the International Conference on Advances in Intelligent Data Analysis,2001. [81] LIU M,LIAO J,WANG J,et al.AGRM:Attention-based graph representation model for telecom fraud detection[C]//Proceedings of the IEEE International Conference on Communications(ICC),2019. [82] SHU K,WANG S,LIU H.Beyond news content:The role of social context for fake news detection[C]//Proceedings of the 12th International Conference on Web Search and Data Mining,2019. [83] MISHRA P,DEL TREDICI M,YANNAKOUDAKIS H,et al.Abusive language detection with graph convolutional networks[J].arXiv:1904.04073,2019. [84] CHANDRA S,MISHRA P,YANNAKOUDAKIS H,et al.Graph-based modeling of online communities for fake news detection[J].arXiv:2008.06274,2020. |
[1] | GAO Guangshang. Survey on Attention Mechanisms in Deep Learning Recommendation Models [J]. Computer Engineering and Applications, 2022, 58(9): 9-18. |
[2] | JI Meng, HE Qinglong. AdaSVRG: Accelerating SVRG by Adaptive Learning Rate [J]. Computer Engineering and Applications, 2022, 58(9): 83-90. |
[3] | LUO Xianglong, GUO Huang, LIAO Cong, HAN Jing, WANG Lixin. Spatiotemporal Short-Term Traffic Flow Prediction Based on Broad Learning System [J]. Computer Engineering and Applications, 2022, 58(9): 181-186. |
[4] | Alim Samat, Sirajahmat Ruzmamat, Maihefureti, Aishan Wumaier, Wushuer Silamu, Turgun Ebrayim. Research on Sentence Length Sensitivity in Neural Network Machine Translation [J]. Computer Engineering and Applications, 2022, 58(9): 195-200. |
[5] | CHEN Yixiao, Alifu·Kuerban, LIN Wenlong, YUAN Xu. CA-YOLOv5 for Crowded Pedestrian Detection [J]. Computer Engineering and Applications, 2022, 58(9): 238-245. |
[6] | FANG Yiqiu, LU Zhuang, GE Junwei. Forecasting Stock Prices with Combined RMSE Loss LSTM-CNN Model [J]. Computer Engineering and Applications, 2022, 58(9): 294-302. |
[7] | SHI Jie, YUAN Chenxiang, DING Fei, KONG Weixiang. Survey of Building Target Detection in SAR Images [J]. Computer Engineering and Applications, 2022, 58(8): 58-66. |
[8] | XIONG Fengguang, ZHANG Xin, HAN Xie, KUANG Liqun, LIU Huanle, JIA Jionghao. Research on Improved Semantic Segmentation of Remote Sensing [J]. Computer Engineering and Applications, 2022, 58(8): 185-190. |
[9] | YANG Jinfan, WANG Xiaoqiang, LIN Hao, LI Leixiao, YANG Yanyan, LI Kecen, GAO Jing. Review of One-Stage Vehicle Detection Algorithms Based on Deep Learning [J]. Computer Engineering and Applications, 2022, 58(7): 55-67. |
[10] | WANG Bin, LI Xin. Research on Multi-Source Domain Adaptive Algorithm Integrating Dynamic Residuals [J]. Computer Engineering and Applications, 2022, 58(7): 162-166. |
[11] | TAN Shuqiu, TANG Guofang, TU Yuanya, ZHANG Jianxun, GE Panjie. Classroom Monitoring Students Abnormal Behavior Detection System [J]. Computer Engineering and Applications, 2022, 58(7): 176-184. |
[12] | ZHANG Meiyu, LIU Yuehui, HOU Xianghui, QIN Xujia. Automatic Coloring Method for Gray Image Based on Convolutional Network [J]. Computer Engineering and Applications, 2022, 58(7): 229-236. |
[13] | ZHANG Zhuangzhuang, QU Licheng, LI Xiang, ZHANG Minghao, LI Zhaolu. Traffic Flow Prediction with Missing Data Based on Spatial-Temporal Convolutional Neural Networks [J]. Computer Engineering and Applications, 2022, 58(7): 259-265. |
[14] | XU Jie, ZHU Yukun, XING Chunxiao. Research on Financial Trading Algorithm Based on Deep Reinforcement Learning [J]. Computer Engineering and Applications, 2022, 58(7): 276-285. |
[15] | SHEN Xulin, LI Chaobo, LI Hongjun. Overview on Video Abnormal Behavior Detection of GAN via Human Density [J]. Computer Engineering and Applications, 2022, 58(7): 21-30. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||