QTTA: Quantum Technologies, Trust and Audit

Bibliometric keyword co-occurrence analysis was performed using Python-based data processing and network analysis techniques. The extracted author keywords were normalized and filtered according to minimum occurrence thresholds to reduce noise and improve interpretability. A weighted co-occurrence network was generated, where nodes represent keywords and edges represent the frequency of co-occurrence within individual publications. Network analysis was conducted using the NetworkX library, while community structures were identified through the Louvain community detection algorithm. The resulting clusters were visualized and interpreted as thematic research domains.