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 by Alessandro Negro

Knowledge Graphs and LLMs in Action
  • MEAP began June 2022
  • Last updated April 2025
  • Publication in August 2025 (estimated)
  • ISBN 9781633439894
  • 425 pages (estimated)
Included with a Manning Online subscription
  • printed in black & white
catalog / Data Science / Machine Learning / Knowledge Graphs

Knowledge Graphs and LLMs in Action is a practical guide to putting knowledge graphs into action. It’s full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets. Throughout the book, you’ll find extensive examples and use-cases taken from healthcare, biomedicine, document archive management systems, and even law enforcement. You’ll learn methodologies based on the very latest KG approaches, as well as deep learning graph techniques such as Graph Neural Networks and NLP-based tools like BERT.

Graph-Powered Machine Learning
  • August 2021
  • ISBN 9781617295645
  • 496 pages
Included with a Manning Online subscription
  • printed in black & white
  • Available translations: 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.