With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
Fabio Montagna is Lead Machine Learning Engineer at GraphAware and presented Temporal Graph Analysis at NODES2022. In this session, we’ll share our experience with horizon scanning over a graph of medical research papers. By leveraging the author keywords from scientific publications, it’s possible to build a cooccurrence graph with a temporal component provided by the paper publication date. We’ll show how we can analyze trends and evolution patterns using an unsupervised algorithm that assigns roles to author keyword.
An introduction to Graph-Powered Machine Learning written by our very own Dr. Alessandro Negro. This book is an extraction of 60 combined years of experience in graphs, and explains how graphs and graph databases can serve machine learning projects.
Are you interested in learning about how machine learning can be leveraged to build a knowledge graph, enabling businesses to differentiate themselves and thrive in today’s competitive marketplace? In this talk, we’ll show you how computer vision, natural language processing and understanding, knowledge enrichment, and graph-native algorithms can be combined to extract valuable insights from various unstructured data sources. Whether you’re a business owner looking to gain a competitive edge or a developer looking to expand your skillset, this talk is sure to be of interest to you.
Graph-based machine learning is a trend in Artificial Intelligence that is gaining popularity due to its many advantages. When using graphs as a basic representation of data for ML purposes, you can benefit from the data being explicitly modeled for further analysis, representing connections and relationships between things and concepts. Additionally, graphs can easily combine multiple sources into a single graph representation and learn from them, creating powerful knowledge graphs. These benefits can lead to improved computation performance and higher quality results. During this talk, you’ll learn about the many benefits of using graph-based machine learning and how it can be applied in the context of recommendation engines and natural language processing. Don’t miss out on this opportunity to learn more about this exciting trend and how it can help you unlock the full potential of your data.