Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Cai, Lei; Chen, Zhengzhang; Luo, Chen; Gui, Jiaping; Ni, Jingchao; Ding, Li; Chen, Haifeng

doi:10.1145/3459637.3481955

Cited by 70 publications

(33 citation statements)

References 45 publications

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“…Graph Neural Networks (GNNs) have been recently proposed to learn the structure of existing relationships between variables while performing anomaly detection in time-series sensor data [39]. GNNs have also been proposed to identify anomalous edges in dynamic graphs [40].…”

Section: Edge Features: Identify Anomalous Connectionsmentioning

confidence: 99%

Self-Supervised and Interpretable Anomaly Detection using Network Transformers

Marino¹,

Wickramasinghe²,

Rieger³

et al. 2022

Preprint

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Section: Edge Features: Identify Anomalous Connectionsmentioning

confidence: 99%

Self-Supervised and Interpretable Anomaly Detection using Network Transformers

Marino¹,

Wickramasinghe²,

Rieger³

et al. 2022

Preprint

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“…In the dynamic link prediction task, methods that learn representations of dynamic networks have been proposed, using deep temporal point processes [43], joint attention mechanisms on nodes neighborhoods and temporal domain [44], memory feature vectors in message-passing architectures [5] or recurrent neural networks [45,1]. For anomalous edge detection in dynamic graphs, [46] process subgraphs around the target edges through convolution and sort pooling operations, and gated recurrent units. To our knowledge, only one prior work has incorporated GNN layers in a method for changepoint detection, but has done so in the context of multivariate time series [47].…”

Section: Related Workmentioning

confidence: 99%

Graph similarity learning for change-point detection in dynamic networks

Sulem¹,

Kenlay²,

Cucuringu³

et al. 2022

Preprint

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Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure. This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring. Leveraging a graph neural network model, we design a method to perform online network change-point detection that can adapt to the specific network domain and localise changes with no delay. The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history. Importantly, our method does not require prior knowledge on the network generative distribution and is agnostic to the type of change-points; moreover, it can be applied to a large variety of networks, that include for instance edge weights and node attributes. We show on synthetic and real data that our method enjoys a number of benefits: it is able to learn an adequate graph similarity function for performing online network change-point detection in diverse types of change-point settings, and requires a shorter data history to detect changes than most existing state-of-the-art baselines.

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“…Li et al [35] proposed AddGraph to detect anomalous edges in a dynamic graph by using a method based on extended temporal GCN with an attention model. Cai et al [36] proposed StrGNN by using GCN and Sortpooling layer to extract the feature and use Gated Recurrent Units (GRU) to capture the temporal information for detecting anomalous edges in dynamic graphs.…”

Section: Related Workmentioning

confidence: 99%

MP-GCN: A Phishing Nodes Detection Approach via Graph Convolution Network for Ethereum

Chen

2022

Applied Sciences

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Blockchain is making a big impact in various applications, but it is also attracting a variety of cybercrimes. In blockchain, phishing transfers the victim’s virtual currency to make huge profits through fraud, which poses a threat to the blockchain ecosystem. To avoid greater losses, Ethereum, one of the blockchain platforms, can provide information to detect phishing fraud. In this study, to effectively detect phishing nodes, we propose a phishing node detection approach as message passing based graph convolution network. We first form a transaction network through the transaction records of Ethereum and then extract the information of nodes effectively via message passing. Finally, we use a graph convolution network to classify the normal and phishing nodes. Experiments show that our method is effective and superior to other existing methods.

show abstract

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Cited by 70 publications

References 45 publications

Self-Supervised and Interpretable Anomaly Detection using Network Transformers

Self-Supervised and Interpretable Anomaly Detection using Network Transformers

Graph similarity learning for change-point detection in dynamic networks

MP-GCN: A Phishing Nodes Detection Approach via Graph Convolution Network for Ethereum

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