316 research outputs found
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
Graphs are used widely to model complex systems, and detecting anomalies in a
graph is an important task in the analysis of complex systems. Graph anomalies
are patterns in a graph that do not conform to normal patterns expected of the
attributes and/or structures of the graph. In recent years, graph neural
networks (GNNs) have been studied extensively and have successfully performed
difficult machine learning tasks in node classification, link prediction, and
graph classification thanks to the highly expressive capability via message
passing in effectively learning graph representations. To solve the graph
anomaly detection problem, GNN-based methods leverage information about the
graph attributes (or features) and/or structures to learn to score anomalies
appropriately. In this survey, we review the recent advances made in detecting
graph anomalies using GNN models. Specifically, we summarize GNN-based methods
according to the graph type (i.e., static and dynamic), the anomaly type (i.e.,
node, edge, subgraph, and whole graph), and the network architecture (e.g.,
graph autoencoder, graph convolutional network). To the best of our knowledge,
this survey is the first comprehensive review of graph anomaly detection
methods based on GNNs.Comment: 9 pages, 2 figures, 1 tables; to appear in the IEEE Access (Please
cite our journal version.
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple
modalities to a new subspace optimized for one-class classification. The
proposed method iteratively transforms the data from the original feature space
of each modality to a new common feature space along with finding a joint
compact description of data coming from all the modalities. For data in each
modality, we define a separate transformation to map the data from the
corresponding feature space to the new optimized subspace by exploiting the
available information from the class of interest only. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary
material (27 tables, 10 figures). The manuscript and supplementary material
are combined as a single .pdf (50 pages) fil
Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection
Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts,
which has been studied for several decades. At the same time, deep learning has
shown its capacity in solving many graph-based tasks like, node classification,
link prediction, and graph classification. Recently, many studies are extending
graph learning models for solving anomaly analytics problems, resulting in
beneficial advances in graph-based anomaly analytics techniques. In this
survey, we provide a comprehensive overview of graph learning methods for
anomaly analytics tasks. We classify them into four categories based on their
model architectures, namely graph convolutional network (GCN), graph attention
network (GAT), graph autoencoder (GAE), and other graph learning models. The
differences between these methods are also compared in a systematic manner.
Furthermore, we outline several graph-based anomaly analytics applications
across various domains in the real world. Finally, we discuss five potential
future research directions in this rapidly growing field
Graph learning for anomaly analytics : algorithms, applications, and challenges
Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery
Graph-based Time-Series Anomaly Detection: A Survey
With the recent advances in technology, a wide range of systems continue to
collect a large amount of data over time and thus generate time series.
Time-Series Anomaly Detection (TSAD) is an important task in various
time-series applications such as e-commerce, cybersecurity, vehicle
maintenance, and healthcare monitoring. However, this task is very challenging
as it requires considering both the intra-variable dependency and the
inter-variable dependency, where a variable can be defined as an observation in
time series data. Recent graph-based approaches have made impressive progress
in tackling the challenges of this field. In this survey, we conduct a
comprehensive and up-to-date review of Graph-based TSAD (G-TSAD). First, we
explore the significant potential of graph representation learning for
time-series data. Then, we review state-of-the-art graph anomaly detection
techniques in the context of time series and discuss their strengths and
drawbacks. Finally, we discuss the technical challenges and potential future
directions for possible improvements in this research field.Comment: 19 pages, 4 figures, 2 table
Restricted Generative Projection for One-Class Classification and Anomaly Detection
We present a simple framework for one-class classification and anomaly
detection. The core idea is to learn a mapping to transform the unknown
distribution of training (normal) data to a known target distribution.
Crucially, the target distribution should be sufficiently simple, compact, and
informative. The simplicity is to ensure that we can sample from the
distribution easily, the compactness is to ensure that the decision boundary
between normal data and abnormal data is clear and reliable, and the
informativeness is to ensure that the transformed data preserve the important
information of the original data. Therefore, we propose to use truncated
Gaussian, uniform in hypersphere, uniform on hypersphere, or uniform between
hyperspheres, as the target distribution. We then minimize the distance between
the transformed data distribution and the target distribution while keeping the
reconstruction error for the original data small enough. Comparative studies on
multiple benchmark datasets verify the effectiveness of our methods in
comparison to baselines
Raising the Bar in Graph-level Anomaly Detection
Graph-level anomaly detection has become a critical topic in diverse areas,
such as financial fraud detection and detecting anomalous activities in social
networks. While most research has focused on anomaly detection for visual data
such as images, where high detection accuracies have been obtained, existing
deep learning approaches for graphs currently show considerably worse
performance. This paper raises the bar on graph-level anomaly detection, i.e.,
the task of detecting abnormal graphs in a set of graphs. By drawing on ideas
from self-supervised learning and transformation learning, we present a new
deep learning approach that significantly improves existing deep one-class
approaches by fixing some of their known problems, including hypersphere
collapse and performance flip. Experiments on nine real-world data sets
involving nine techniques reveal that our method achieves an average
performance improvement of 11.8% AUC compared to the best existing approach.Comment: To appear in IJCAI-ECAI 202
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