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.

Is it better to have your work team fully connected or sparsely connected?

In this episode we'll try to answer this question and more with our guest Hiroki Sayama, a SUNY Distinguished Professor and director of the Center for Complex Systems at Binghamton University.

Hiroki delves into the applications of network science in organizational structures and innovation dynamics by showing his recent work of extracting network structures from organizational charts to enable insights into decision-making and performance, He'll also cover how network connectivity impacts team creativity and innovation.

Key insights include how the structure of organizational networks—such as the depth of hierarchy or proximity to leadership—can influence corporate performance and how sparse network connectivity fosters more diverse and innovative ideas than fully connected networks.

A man goes into a bar… This is the beginning of a riddle that our guest, Yoed Kennet, an assistant professor at the Technion's Faculty of Data and Decision Sciences, uses to measure creativity in subjects.

In our talk, Yoed speaks about how to combine cognitive science and network science to explore the complexities and decode the mysteries of the human mind.

The listeners will learn how network science provides tools to map and analyze human memory, revealing how problem-solving and creativity emerge from changes in semantic memory structures.

Key insights include the role of memory restructuring during moments of insight, the connection between semantic networks and creative thinking, and how understanding these processes can improve problem-solving and analogical reasoning.

Real-life applications span enhancing creativity in the workplace, building tools to combat cognitive rigidity in aging, and improving learning strategies by fostering richer, more flexible mental networks.

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In this episode, Garima Agrawal, a senior researcher and AI consultant, brings her years of experience in data science and artificial intelligence. Listeners will learn about the evolving role of knowledge graphs in augmenting large language models (LLMs) for domain-specific tasks and how these tools can mitigate issues like hallucination in AI systems.

Key insights include how LLMs can leverage knowledge graphs to improve accuracy by integrating domain expertise, reducing hallucinations, and enabling better reasoning.

Real-life applications discussed range from enhancing customer support systems with efficient FAQ retrieval to creating smarter AI-driven decision-making pipelines.

Garima’s work highlights how blending static knowledge representation with dynamic AI models can lead to cost-effective, scalable, and human-centered AI solutions.

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In this episode, Bnaya Gross, a Fulbright postdoctoral fellow at the Center for Complex Network Research at Northwestern University, explores the transformative applications of network science in fields ranging from infrastructure to medicine, by studying the interactions between networks ("a network of networks").

Listeners will learn how interdependent networks provide a framework for understanding cascading failures, such as power outages, and how these insights transfer to physical systems like superconducting materials and biological networks.

Key takeaways include understanding how dependencies between networks can amplify vulnerabilities, applying these principles to create resilient infrastructure systems, and using network medicine to uncover relationships between diseases, potential drug repurposing and the process of aging.

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Our guests, Erwan Le Merrer and Gilles Tredan, are long-time collaborators in graph theory and distributed systems. They share their expertise on applying graph-based approaches to understanding both large language model (LLM) hallucinations and shadow banning on social media platforms.

In this episode, listeners will learn how graph structures and metrics can reveal patterns in algorithmic behavior and platform moderation practices.

Key insights include the use of graph theory to evaluate LLM outputs, uncovering patterns in hallucinated graphs that might hint at the underlying structure and training data of the models, and applying epidemic models to analyze the uneven spread of shadow banning on Twitter.

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In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based techniques for cybersecurity applications.

We'll learn how graphs are used to detect malicious activity in networks, such as identifying harmful domains and executable files by analyzing their relationships within vast datasets.

This will include the use of hierarchical multi-instance learning (HML) to represent JSON-based network activity as graphs and the advantages of analyzing connections between entities (like clients, domains etc.).

Our guest shows that while other graph methods (such as GNN or Label Propagation) lack in scalability or having trouble with heterogeneous graphs, his method can tackle them because of the "locality assumption" – fraud will be a local phenomenon in the graph – and by relying on this assumption, we can get faster and more accurate results.

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Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply chain operations. In this episode, listeners will learn how graph-based approaches can transform supply chains by enabling more efficient routing, districting, and decision-making in complex logistical networks.

Key insights include the application of Graph Neural Networks to predict delivery costs, with potential to improve districting strategies for companies like UPS or Amazon and overcoming limitations of traditional heuristic methods.

Thibaut’s work underscores the potential for GNN to reduce costs, enhance operational efficiency, and provide better working conditions for teams through improved route familiarity and workload balance.