Organised crime group detection using community detection

In most cases, existing definitions in the literature on organised crime concentrate on three essential perspectives for characterising the nature of this form of crime (von Lampe, 2019). First, organised crime is primarily about crime, such that organised crime is seen as a specific type of criminal activity that has ceriain specific characteristics (continuity, in contrast to irregular criminal behaviour). Second, organised crime is related to the concentration of power, either in economic or in political structures of the society. Thu d, the emphasis is on being organised. That is, the important aspect of organised crime is on how offenders are connected to each other more than what they do.

Based on the thu d view, we can formalise central aspects of criminal networks in a coherent and consistent formal framework to provide a precise semantic foundation that is consistent with criminological research, social network analysis, and law enforcement operations. Such work would ami at bridging the conceptual gap between data level, mining level and interpretation level, and is intended for developing advanced computational methods for analysing co-offending networks to detect and extract organised crime structures and how they evolve over time in order to assist law enforcement and intelligence agencies in their investigations.

Community detection in social networks has attracted considerable interest and many definitions of the concept of community have been proposed. In social science studies, social networks are considered as basis of social behaviours and activities. Studies of different social networks show that community structure influences information transfer, communication, and cooperation. Sense of community is generally defined as a feeling that members of a group matter to one another and to the group, and a common belief that members’ needs will be satisfied through their commitment to be together. The nature of organised crime groups, however, is different from other types of groups such as friendship or co-authorship groups. Organised crime groups are usually well established with group membership being defined explicitly and strictly. Unlike friendship or coauthorship communities, offender groups as well are characterised by member relationships that are more systematic and organised to achieve material benefit from committing crime. Therefore, detecting organised crime groups calls for a stricter definition of community. Given the large volumes of law enforcement data, analysing and extracting knowledge from their data using data-driven approaches such as machine learning, as discussed earlier, can help define what matters for organised crime groups that is different from other forms of groups to better identify their presence.

On this note, and based on fundamental discussions in the criminology literature, one can summarise the important characteristics of organised crime groups as follows: 1) these groups have at least three members and can be categorised as centralised or distributed or hierarchical groups, but the focus is on offender groups for which the density of their intra-group collaborations is higher than the density of intergroup collaborations; 2) organised crime groups are characterised by a distribution of roles and differ ent degr ees of agency among individuals, where groups can overlap and may have common members; 3) these groups commit serious crimes with the perspective of gaining material benefit; and 4) then-activity is more continuous compared to regular offender groups. Using big data analytics, these characteristics of organised crime groups can be searched and then tested with regard to their validity on a grand scale with all possible combinations. Moreover, pr edictive analytics can then be used to identify the potential emergence of these characteristics in real time.

Therefore, for the pur-pose of organised crime grortp detection, in each time snapshot of a co-offending network the following tasks are carried out in consecutive steps: 1) discover offender groups in the current network; 2) comprtte the activity and criminality of these grortps in the time period between the current network and the previous network based on the offences that were committed by then- members; 3) assess the material benefit associated with each of the offences considered in Step 2; 4) identify those groups that qualify as possible criminal organisations; 5) update the groups evolutionary trace for the current time period. It should be clear at this point that sophisticated data mining techniques become critical with the increasingly complexity of such analyses. For example, rather than relying on traditional policing methods to identify organised crime groups, police calls for service data with co-offender identifiers can be used to identify clusters of individuals who may then be assigned to these more formal group relationships. But before such analyses can take place, identifying all co-offences is critical.