  This paper presents TransCrimeNet, a novel transformer-based model for
predicting future crimes in criminal networks from textual data. Criminal
network analysis has become vital for law enforcement agencies to prevent
crimes. However, existing graph-based methods fail to effectively incorporate
crucial textual data like social media posts and interrogation transcripts that
provide valuable insights into planned criminal activities. To address this
limitation, we develop TransCrimeNet which leverages the representation
learning capabilities of transformer models like BERT to extract features from
unstructured text data. These text-derived features are fused with graph
embeddings of the criminal network for accurate prediction of future crimes.
Extensive experiments on real-world criminal network datasets demonstrate that
TransCrimeNet outperforms previous state-of-the-art models by 12.7\% in F1
score for crime prediction. The results showcase the benefits of combining
textual and graph-based features for actionable insights to disrupt criminal
enterprises.
