Analysis of Criminal Networks in Python

• When: March 5, 2026 @ 1200 US Eastern Time
• Instructor: Ruslan Klymentiev
• Cost: FREE for all attendees
• Register Here

This webinar introduces criminal network analysis using Python, focusing on co-offending networks derived from police data. Participants will follow the analytical workflow, from raw police data to the construction and analysis of a social network of co-offenders. The aim is to demonstrate how routinely collected police records can be transformed into network representations that reveal how offenders are connected through shared criminal activity.

The webinar then focuses on individual-level analysis, illustrating how to identify prolific and structurally important offenders. Ego-centric networks are extracted to examine the immediate criminal environment of selected individuals. Centrality measures are introduced and interpreted to assess which actors occupy key structural positions, including potential brokers between groups and individuals who may be particularly relevant for network disruption strategies. Additionally, the webinar introduces the concept of multilayer networks to illustrate how different types of relationships or interactions can be modeled and analyzed.

In addition to network analysis, the webinar highlights practical data manipulation techniques in Python that are commonly required when working with police data. All examples are implemented using open-source Python libraries, including Pandas for data manipulation, NetworkX for network construction and analysis, and PyViz for network visualization. No prior knowledge of social network analysis or Python programming is required, although familiarity with data analysis concepts may be advantageous.

All registrants who are IACA members will receive Continuing Education Credits and you may request a certificate if you want one.

If you have questions about the webinar program, please view our FAQs page or email [email protected].