In this episode we talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London, who explores how network science helps uncover criminal networks.

Justin is also a member of the organizing committee of the satellite conference dealing with criminal networks at the network science conference in The Netherlands in June 2025.

Listeners will learn how graph-based models assist law enforcement in analyzing missing data, identifying key figures in criminal organizations, and improving intervention strategies.

Key insights include the challenges of incomplete and inaccurate data in criminal network analysis, how law enforcement agencies use network dismantling techniques to disrupt organized crime, and the role of machine learning in predicting hidden connections within illicit networks.

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In this episode today’s guest is Celine Wüst, a master’s student at ETH Zurich specializing in secure and reliable systems, shares her work on automated software testing for graph databases. Celine shows how fuzzing—the process of automatically generating complex queries—helps uncover hidden bugs in graph database management systems like Neo4j, FalconDB, and Apache AGE.

Key insights include how state-aware query generation can detect critical issues like buffer overflows and crashes, the challenges of debugging complex database behaviors, and the importance of security-focused software testing.

We'll also find out which Graph DB company offers swag for finding bugs in its software and get Celine's advice about which graph DB to use.

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In this episode, Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organizational structures using OrgXO, a tool that maps companies as networks rather than rigid hierarchies. Listeners will learn how analyzing workplace collaboration networks can reveal hidden influencers, organizational bottlenecks, and engagement levels, offering a data-driven approach to improving effectiveness and resilience.

Key insights include how companies can identify overburdened employees, address silos between departments, and detect vulnerabilities where too few individuals hold critical knowledge. Real-life applications range from mergers and acquisitions, where network analysis helps assess company dynamics before an acquisition, to restructuring efforts that improve workflow and team collaboration.

Gabriel’s work highlights how organizations can shift from traditional hierarchical thinking to a network-based perspective, leading to smarter decision-making and more adaptable companies.