Abstract: Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ...

In this work, to address this issue, knowledge graph technology is used to build an anomaly detection dataset. Considering the over-smoothing problem associated with multi-level GCN networks, a deep ...

This Research Topic aims to bring together cutting-edge research and practical advancements in the development and application of graph-guided neural networks for anomaly detection in chemical ...

A recent trend of anomaly detection on graph data is to utilize graph neural networks to generate node, path or network embeddings which preserves both network topology structure and node content ...

Abstract: Contrary to the ... security tools cannot deal with. The AEN graph structural foundation can serve as a basis to construct a graph to be used in Graph Neural Network (GNN) for anomaly and ...

The attention_adjacency_gcn_anomaly_detection.ipynb contains code for a Graph Convolutional Neural Network (GCN) in which the graph structure is dynamically learnt using the Multihead Attention ...

In the context of these analyses, Graph Neural Networks (GNNs) emerge as powerful tools for considering the proximity of sample neighbors in anomaly detection and data classification, particularly ...