Anomalies represent rare observations (e.g., data records or events) that
deviate significantly from others. Over several decades, research on anomaly
mining has received increasing interests due to the implications of these
occurrences in a wide range of disciplines. Anomaly detection, which aims to
identify rare observations, is among the most vital tasks in the world, and has
shown its power in preventing detrimental events, such as financial fraud,
network intrusion, and social spam. The detection task is typically solved by
identifying outlying data points in the feature space and inherently overlooks
the relational information in real-world data. Graphs have been prevalently
used to represent the structural 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 database/set of graphs.
However, conventional anomaly detection techniques cannot tackle this problem
well because of the complexity of graph data. For the advent of deep learning,
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. We compile open-sourced implementations, public datasets, and
commonly-used evaluation metrics to provide affluent resources for future
studies. More importantly, we highlight twelve extensive future research
directions according to our survey results covering unsolved and emerging
research problems and real-world applications. 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.

本文综述了现代深度学习技术在图异常检测中的应用，编译了开放资源的实现、公共数据集和常用评估方法，重点强调了 12 个未解决和新兴的研究问题和现实世界的应用。

基于深度学习的图异常检测综述

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Ensemble techniques for classification and clustering have long proven
effective, yet anomaly ensembles have been barely studied. In this work, we tap
into this gap and propose a new ensemble approach for anomaly mining, with
application to event detection in temporal graphs. Our method aims to combine
results from heterogeneous detectors with varying outputs, and leverage the
evidence from multiple sources to yield better performance. However, trusting
all the results may deteriorate the overall ensemble accuracy, as some
detectors may fall short and provide inaccurate results depending on the nature
of the data in hand. This suggests that being selective in which results to
combine is vital in building effective ensembles---hence "less is more".
In this paper we propose SELECT; an ensemble approach for anomaly mining that
employs novel techniques to automatically and systematically select the results
to assemble in a fully unsupervised fashion. We apply our method to event
detection in temporal graphs, where SELECT successfully utilizes five base
detectors and seven consensus methods under a unified ensemble framework. We
provide extensive quantitative evaluation of our approach on five real-world
datasets (four with ground truth), including Enron email communications, New
York Times news corpus, and World Cup 2014 Twitter news feed. Thanks to its
selection mechanism, SELECT yields superior performance compared to individual
detectors alone, the full ensemble (naively combining all results), and an
existing diversity-based ensemble.

本文提出了 SELECT 算法，该算法基于集成学习方法和新颖技术实现异常挖掘，通过自动和系统地选择结果来组装数据，对临时图中的事件进行检测，不仅能显著提高检测效果，并且在五项真实数据集的测试中得到了证实。