A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

被引:338
作者
Ma, Xiaoxiao [1 ]
Wu, Jia [1 ]
Xue, Shan [1 ]
Yang, Jian [1 ]
Zhou, Chuan [2 ]
Sheng, Quan Z. [1 ]
Xiong, Hui [3 ]
Akoglu, Leman [4 ]
机构
[1] Macquarie Univ, Sch Comp, Sydney, NSW 2109, Australia
[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
[3] Rutgers State Univ, Dept Management Sci & Informat Syst, Piscataway, NJ 08854 USA
[4] Carnegie Mellon Univ, Heinz Coll Informat Syst & Publ Policy, Pittsburgh, PA 15213 USA
基金
澳大利亚研究理事会;
关键词
Anomaly detection; outlier detection; fraud detection; rumor detection; fake news detection; spammer detection; misinformation; graph anomaly detection; deep learning; graph embedding; graph representation; graph neural networks; OUTLIER DETECTION; NETWORK; DYNAMICS;
D O I
10.1109/TKDE.2021.3118815
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines - for instance, security, finance, and medicine. For this reason, anomaly detection, which aims to identify these rare observations, has become one of the most vital tasks in the world and has shown its power in preventing detrimental events, such as financial fraud, network intrusions, and social spam. The detection task is typically solved by identifying outlying data points in the feature space, which, inherently, overlooks the relational information in real-world data. At the same time, graphs have been prevalently used to represent the structural/relational information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a set/database of graphs. Conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.). However, thanks to the advent of deep learning in breaking these limitations, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, we provide a taxonomy that follows a task-driven strategy and categorizes existing work according to the anomalous graph objects that they can detect. We especially focus on the challenges in this research area and discuss the key intuitions, technical details as well as relative strengths and weaknesses of various techniques in each category. From the survey results, we highlight 12 future research directions spanning unsolved and emerging problems introduced by graph data, anomaly detection, deep learning and real-world applications. Additionally, to provide a wealth of useful resources for future studies, we have compiled a set of open-source implementations, public datasets, and commonly-used evaluation metrics. With this survey, our goal is to create a "one-stop-shop" that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.
引用
收藏
页码:12012 / 12038
页数:27
相关论文
共 206 条
[1]  
Aggarwal CC, 2001, SIGMOD RECORD, V30, P37
[2]  
Aggarwal CC, 2011, PROC INT CONF DATA, P399, DOI 10.1109/ICDE.2011.5767885
[3]  
Ahmed S., 2019, P AAAI C ART INT, V12, P8
[4]  
Ailon N, 2009, Advances in neural information processing systems, V22, P10
[5]  
Akoglu L., 2021, PROC ACM 30 INT C IN, P1
[6]   Graph based anomaly detection and description: a survey [J].
Akoglu, Leman ;
Tong, Hanghang ;
Koutra, Danai .
DATA MINING AND KNOWLEDGE DISCOVERY, 2015, 29 (03) :626-688
[7]  
Akoglu L, 2010, LECT NOTES ARTIF INT, V6119, P410
[8]  
Akoglu L, 2009, LECT NOTES ARTIF INT, V5781, P13, DOI 10.1007/978-3-642-04180-8_13
[9]  
Alsentzer E., 2020, P INT C NEUR INF PRO
[10]  
[Anonymous], 2017, PROC INT AAAI C WEB, DOI DOI 10.1609/ICWSM.V11I1.14887