Anomaly Detection in Dynamic Graphs Using Multiple Encoding Strategies via Transformers

doi:10.1109/TCE.2025.3573163

Anomaly Detection in Dynamic Graphs Using Multiple Encoding Strategies via Transformers

2025-05-01

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DOI：10.1109/TCE.2025.3573163

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Accurate anomaly detection in dynamic graph networks suffers due to lack of coverage of all aspects of information; specifically temporal, spatial and centrality based cross-coupled information. This work aims to address the challenge of precise and accurate anomaly detection in dynamic graph networks. It uses a graph-based diffusion technique to sample a fixed-size, yet cross-coupled, information-rich circumstantial node set for target edges. Centrality enabled spatial-temporal node encoding is considered as input to the dynamic graph based transformer network. The proposed method uses a set of four elements to make up the node encoding. The four encoding terms are combined to create an input that contains extensive centrality based cross-coupled spatial-temporal node encoding. The transformer module simultaneously captures all the required attributes with a single encoder. The performance of the proposed method is validated on six different datasets; UCI Messages, Bitcoin-Alpha, Digg Social, Enron Email, Epinions-Trust and AS-Topology. The proposed method outperforms the existing methods in terms of AUC-ROC score, accuracy, loss, and precision. Results show an improvement of 2.42% AUC-ROC value over the existing methods proving the models ability to counter over-fitting and provide accurate results.

Keywords:

Anomaly detection

dynamic graphs

edge networks

transformers

input embeddings

Anomaly Detection in Dynamic Graphs Using Multiple Encoding Strategies via Transformers

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