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This paper presents TransCrimeNet, a novel transformer-based model forpredicting future crimes in criminal networks from textual data. Criminalnetwork analysis has become vital for law enforcement agencies to preventcrimes. However, existing graph-based methods fail to effectively incorporatecrucial textual data like social media posts and interrogation transcripts thatprovide valuable insights into planned criminal activities. To address thislimitation, we develop TransCrimeNet which leverages the representationlearning capabilities of transformer models like BERT to extract features fromunstructured text data. These text-derived features are fused with graphembeddings of the criminal network for accurate prediction of future crimes.Extensive experiments on real-world criminal network datasets demonstrate thatTransCrimeNet outperforms previous state-of-the-art models by 12.7\% in F1score for crime prediction. The results showcase the benefits of combiningtextual and graph-based features for actionable insights to disrupt criminalenterprises.

TransCrimeNet: A Transformer-Based Model for Text-Based Crime Prediction in Criminal Networks