Research Area:  Machine Learning

Abstract:

Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines. Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data in the features space and inherently overlooks the structural information. Graphs have been prevalently used to preserve structural information, and this raises the graph anomaly detection problem - identifying anomalous graph objects (nodes, edges, sub-graphs, and graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data. For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We also highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.

Keywords:  
Anomaly Detection
Graph Anomaly Detection
Deep Learning
Graph Mining
Graph Neural Networks

Author(s) Name:  Xiaoxiao Ma; Jia Wu; Shan Xue; Jian Yang; Chuan Zhou; Quan Z. Sheng; Hui Xiong; Leman Akoglu

Publisher name:  IEEE

DOI:  10.1109/TKDE.2021.3118815

Volume Information:   Page(s): 1 - 1

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9565320