GATAE: Graph Attention-based Anomaly Detection on Attributed Networks

You, Ziquan; Gan, Xiaoying; Fu, Luoyi; Wang, Zhen

doi:10.1109/iccc49849.2020.9238879

“…The DOMINANT approach [17] models attributed networks by designing a deep learning model for anomaly detection. GATAE method [18] uses an attention mechanism to better learn the representation of nodes for anomaly detection.…”

Section: A Homogeneous Network Anomaly Detectionmentioning

confidence: 99%

MVAD HAN: A Multi-View Based Anomaly Detection Method for Heterogeneous Attributed Networks

Han,

Qin

2023

J-NaNA

0

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With the frequent occurrence of network security incidents in recent years, it has become very important to detect anomalous behaviour in networks as early and accurately as possible. Anomaly detection can improve the security of complex network systems by detecting abnormal and unreliable nodes, and thus it has become a hot research direction that has attracted wide attention. At present, abstracting real complex systems into complex networks for anomaly detection is the mainstream research method. However, the existing methods still have challenges in extracting network heterogeneity information and attribute information, so we propose a multi-view based anomaly detection method for heterogeneous attributed networks, MVAD HAN. This method can better extract the heterogeneous structural information and rich attribute information of the network to model heterogeneous attributed networks. Our method adopts an encoder-decoder architecture. First, in the encoder part, we use the Heterogeneous Graph Transformer with multiple views to learn node embeddings that fuse the heterogeneous information of the network. In the decoder part, we use an inner product decoder to reconstruct the network topology, a multilayer perceptron-based decoder to better reconstruct the network attribute information, and a linear projection to reconstruct the node type information of the network. Finally, we compute an anomaly score for each node using three reconstruction errors: network structure, attributes and node type. The higher the reconstruction error of a node, the higher the anomaly score and the higher the probability of an anomaly. Finally, anomalous nodes are identified by ranking the anomaly scores and setting a threshold. We validate the effectiveness of the proposed method on four real-world datasets. The experimental results show that this method outperforms several of the baseline methods and has a good performance in anomaly detection.

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“…Semi-GCN [27] Label information → semi-supervised learning by GCN HCM [28] Label & contextual information → hop-count prediction model ResGCN [29] Over-smoothing issue → GCN with residual-based attention CoLA [30] Targeting issue of GAE → contrastive self-supervised learning ANEMONE [31] Contextual information → multi-scale contrastive learning PAMFUL [32] Contextual information → pattern mining algorithm with GCN GAT-based GAE AnomalyDAE [33] Complex interactions → GAT-based encoder GATAE [34] Over-smoothing issue → GAT-based encoder AEGIS [35] Handling unseen nodes → generative adversarial learning with GAE…”

Section: Gcn Alonementioning

confidence: 99%

“…Moreover, to alleviate the over-smoothing issue in GCNs, the graph attention-based AE (GATAE) [34] embedded the input attributed graph by using multiple graph attention layers as an encoder in order to detect global and structural anomalies. An inner product decoder was used for reconstruction of the graph structure, and a decoder with the same architecture as its encoder was used to reconstruct node attributes.…”

Section: C: Gat-based Gae Frameworkmentioning

confidence: 99%

Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges

Kim

¹

,

Lee

²

,

Shin

³

et al. 2022

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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.

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“…Contextual information → pattern mining algorithm with GCN GAT-based GAE AnomalyDAE [33] (2020) Complex interactions → GAT-based encoder GATAE [34] (2020)…”

Section: A Gnn-based Static Graph Anomaly Detectionmentioning

confidence: 99%

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

Kim¹,

Lee²,

Shin³

et al. 2022

Preprint

0

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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.

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GATAE: Graph Attention-based Anomaly Detection on Attributed Networks

Cited by 8 publications

References 16 publications

MVAD HAN: A Multi-View Based Anomaly Detection Method for Heterogeneous Attributed Networks

MVAD HAN: A Multi-View Based Anomaly Detection Method for Heterogeneous Attributed Networks

Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

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