Graph based anomaly detection and description: a survey
TLDR
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs, and gives a general framework for the algorithms categorized under various settings.Abstract:
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.read more
Citations
More filters
Journal ArticleDOI
Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey
TL;DR: A comprehensive survey on the most popular feature encoding and pooling approaches in action and event recognition in recent years is offered by summarizing systematically both underlying theoretical principles and original experimental conclusions of those approaches based on an approach-based taxonomy so as to provide impetus for future relevant studies.
Proceedings ArticleDOI
A Flexible Attentive Temporal Graph Networks for Anomaly Detection in Dynamic Networks
Dali Zhu,Ma Yuchen,Yinlong Liu +2 more
TL;DR: In this article, the authors proposed a novel framework DynAD for anomaly detection on time-evolving networks, which performs adaptive parameter learning in an end-to-end manner.
Posted Content
Few-shot Network Anomaly Detection via Cross-network Meta-learning
TL;DR: Wang et al. as discussed by the authors proposed Graph Deviation Networks (GDN) which can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and equipping the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks.
Journal ArticleDOI
ServiceRank: Root Cause Identification of Anomaly in Large-Scale Microservice Architectures
TL;DR: ServiceRank as discussed by the authors is a framework for anomaly detection and root cause identification in the microservice architecture to tackle the challenges of increasing business applications running in the cloud, and it can deploy rapidly and easily in various systems without any pre-defined knowledge.
Journal ArticleDOI
Communities as Vague Operators: Epistemological Questions for a Critical Heuristics of Community Detection Algorithms
TL;DR: In this article , the authors analyse the nature and epistemic consequences of what figures in network science as patterns of nodes and edges called "communities" and propose to describe the concept of community as a "vague operator", a variant of Susan Leigh Star's notion of the boundary object.
References
More filters
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.