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
Book ChapterDOI
Anomaly Detection in Networks with Temporal Information
TL;DR: A technique for node anomaly detection in networks where arcs are annotated with time of creation by taking simultaneously into account information concerning both the structure of the network and the order in which connections have been established.
Journal ArticleDOI
Privacy-preserving data mining of cross-border financial flows
TL;DR: Wang et al. as discussed by the authors developed a novel measure for detecting anomalies in cross-border financial networks, allowing financial institutions and regulatory organizations to identify suspicious nodes, and used a sample dataset comprising international financial transactions and a hypothetical dataset to demonstrate the measure of node importance and symmetric-key encryption algorithm.
Book ChapterDOI
Vienna Graph Clustering
TL;DR: A key component of this contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques that combine with a scalable communication protocol to produce a system that is able to compute high-quality solutions in a short amount of time.
Posted Content
Graph Fairing Convolutional Networks for Anomaly Detection.
Mahsa Mesgaran,A. Ben Hamza +1 more
TL;DR: A simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection, which exploits both the graph structure and node features for learning discriminative node representations.
Proceedings ArticleDOI
Detecting Anomalous Swarming Agents With Graph Signal Processing
TL;DR: In this paper, anomalous agents can be effectively detected using their impacts on the graph Fourier structure of the swarm by defining a graph structure between agents in a swarm and using tools from the field of graph signal processing to understand local and global swarm properties.
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.