Alessandro Negro

First and foremost, I am immensely passionate about computer science and data research. I specialize in NLP, recommendation engines, fraud detection, and graph-aided search.

After pursuing computer engineering academically and working in various capacities in the domain, I pursued my PhD in Interdisciplinary Science and Technology. With my interest in graph databases peaking, I founded a company called Reco4, which aimed to support an open source project called reco4j—the first recommendation framework based on graph data sources.

Now I’m Chief Scientist at GraphAware, where we are all driven by the goal of being the first name in graph technologies. With clients such as LinkedIn, the World Economic Forum, the European Space Agency, and Bank of America, we are singularly focused on helping clients gain a competitive edge by transforming their data into searchable, understandable, and actionable knowledge. In the past few years, I have spent my time leading the development of Hume (our knowledge graph platform) and speaking at various conferences around the world.

books & videos by Alessandro Negro

Knowledge Graphs and LLMs in Action

  • October 2025
  • ISBN 9781633439894
  • 472 pages
  • printed in black & white
  • available in Russian, Simplified Chinese

Knowledge Graphs and LLMs in Action shows you how to introduce knowledge graphs constructed from structured and unstructured sources into LLM-powered applications and RAG pipelines. Real-world case studies for domain-specific applications—from healthcare to financial crime detection—illustrate how this powerful pairing works in practice. You’ll especially appreciate the expert insights on knowledge representation and reasoning strategies.

Advantages of Graph-Based Machine Learning Systems

  • Course duration: 52m

How do you apply graphs to machine-learning projects such as recommendation engines and chatbots?

Graph-Powered Machine Learning

  • August 2021
  • ISBN 9781617295645
  • 496 pages
  • printed in black & white
  • available in Simplified Chinese

Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.