Graph Neural Network Based Anomaly Detection in Dynamic Networks

被引：0

作者：

Guo J.-Y. ^{[1
]}

Li R.-H. ^{[2
]}

Zhang Y. ^{[1
]}

Wang G.-R. ^{[2
]}

机构：

[1] School of Electronics Engineering and Computer Science, Peking University, Beijing

[2] School of Computer Science and Technology, Beijing Institute of Technology, Beijing

来源：

Ruan Jian Xue Bao/Journal of Software | 2020年 / 31卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Anomaly detection in dynamic network; Deep learning on graphs; Graph neural network;

D O I：

10.13328/j.cnki.jos.005903

中图分类号：

学科分类号：

摘要：

Dynamic graph structured data is ubiquitous in real-life applications. Mining outliers on dynamic networks is an important problem, which is very useful for many practical applications. Most traditional network outlier detection algorithms focus mainly on the strutraulal anomaly, ignoring the nodes and edges' attributes, and the time-varying features as well. This study proposes a graph neural network based network anomaly detection algorithm which can capture the nodes and edges' attributes and time-varying features and fully uses these features to learn a representation vector for each node. Specifically, the proposed algorithm improves an unsupervised graph neural network framework called DGI. Based on DGI, a new danamic DGI algorithm is proposed, which is called Dynamic-DGI, for dynamic networks. Dynamic-DGI can simultaneously extracts the abnormal characteristics of the network itself and the abnormal characteristics of the network changes. The experimental results show that the proposed algorithm is better than the state-of-the-art anomaly detection algorithm SpotLight, and is significantly better than the traditional network representation learning algorithms. In addition to improving the accuracy, the proposed algorithmis also able to mine interesting anomalies in the network. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

引用

页码：748 / 762

页数：14

共 38 条

[1]

Carley K.M., Dynamic Network Analysis, (2003)

[2]

Eswaran D., Faloutsos C., Sedanspot: Detecting anomalies in edge streams, Proc. of the Int'l Conf. on Data Mining (ICDM), pp. 953-958, (2018)

[3]

Gupta M., Gao J., Sun Y., Et al., Integrating community matching and outlier detection for mining evolutionary community outliers, Proc. of the 9th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), pp. 859-867, (2012)

[4]

Heard N.A., Weston D.J., Platanioti K., Et al., Bayesian anomaly detection methods for social networks, The Annals of Applied Statistics, 4, 2, pp. 645-662, (2010)

[5]

Noble C.C., Cook D.J., Graph-based anomaly detection, Proc. of the 9th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), pp. 631-636, (2013)

[6]

Ranshous S., Shen S., Koutra D., Et al., Anomaly detection in dynamic networks: A survey, Wiley Interdisciplinary Reviews: Computational Statistics, 7, 3, pp. 223-247, (2015)

[7]

Savage D., Zhang X., Yu X., Et al., Anomaly detection in online social networks, Social Networks, 39, pp. 62-70, (2014)

[8]

Akoglu L., Tong H., Koutra D., Graph based anomaly detection and description: A survey, Data Mining and Knowledge Discovery, 29, 3, pp. 626-688, (2015)

[9]

Perozzi B., Al-Rfou R., Skiena S., Deepwalk: Online learning of social representations, Proc. of the 20th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), pp. 701-710, (2014)

[10]

Guha S., Mishra N., Roy G., Et al., Robust random cut forest based anomaly detection on streams, Proc. of the Int'l Conf. on Machine Learning (ICML), pp. 2712-2721, (2016)

← 1 2 3 4 →