Data Skeptic Criminal Networks
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Mar 17, 2025 In this discussion, Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute in London, delves into the intersection of network science and crime. He reveals how graph-based models can uncover key figures in criminal organizations and optimize law enforcement strategies. Key topics include the challenges of inaccurate data, innovative interventions to dismantle networks, and the role of machine learning in revealing hidden connections. Justin also highlights the need for improved data collection methods to better understand organized crime.
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Data Sufficiency for Network Analysis
- Justin Wang Ngai Yeung emphasizes needing 60-80% network data for accurate size estimations.
- Asaf disagrees, citing Christakis's work showing 5% can yield actionable intelligence using the friends paradox.
Identifying Hubs
- Asaf and Kyle discuss identifying hubs in networks, like a community organizer.
- Community organizers are hubs because they know many people, making them easy to identify.
Importance of High-Degree Nodes
- Justin Wang Ngai Yeung's example of a delivery person in a dataset highlighted that high-degree nodes aren't always key figures.
- Asaf counters that even delivery people can be important leads or hubs.