Retrieving criminal ties and mining evidence from an organised crime incident, for example money laundering, has been a difficult task for crime investigators due to the involvement of different groups of people and their complex relationships. Extracting the criminal associations from enormous amounts of raw data and representing them explicitly is tedious and time consuming [1, 6, 13] . A study of the complex network literature reveals that graph-based detection methods have not, as yet, been

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... used for money laundering detection. In this research, I explore the use of complex network analysis to identify the communication associations of money laundering criminals, that is, the important people who communicate between known criminals and the reliance of the known criminals on the other individuals in a communication path. For this purpose I use the publicly available Enron email database [5] that happens to contain the communications of 10 criminals who were convicted of money laundering crime [3] . I show that my new shortest paths network search algorithm (SPNSA) combining shortest paths and network centrality measures is better able to isolate and identify criminals' connections when compared with existing community detection algorithms and k-neighbourhood detection [9] . The SPNSA is validated using three different scenarios: (i) when the investigator knows all the criminals, (ii) when the investigator fails to detect one of the criminals and (iii) when the investigator is at the starting stage and does not have any information about the criminals, but suspects a crime is occurring. In each of these scenarios, the criminal network graphs formed using SPNSA are small and sparse and hence suitable for further investigation. The SPNSA algorithm manages to extract a criminal network with a minimum of four criminals when none of the criminals is known.