Anomaly Detection in Dynamic Graphs Using Multiple Encoding Strategies via Transformers

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.