Resources - ML

Videos, Slides, Case Studies and other GraphAware related resources

Graph-Powered Machine Learning Q&A

17 Nov 2021 videos graphs ML

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.

Unparalleled Graph Database Scalability Delivered by Neo4j 4.0 - Graph Powered Machine Learning

04 Apr 2020 slides Neo4j ML graphs

Presentation by Dr. Alessandro Negro, Chief Scientist at GraphAware and author of the Manning’s book Graph-powered machine learning, that covers the following topics:

Why unlimited scale is important when using graph databases

The new graph database scaling capabilities built by Neo4j developers

The role of graphs to support machine learning application

How Neo4j assists customers in scaling their applications

Concrete examples of machine learning projects that can leverage graph sharding

The recording is available as well: https://bit.ly/39ZqFVE

Graph-Powered Machine Learning - Book

10 Jan 2020 publications ML graphs

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.

Get the book

Social media monitoring with ML-powered Knowledge Graph

10 Oct 2019 videos ML KG

Ever wondered how ML can be used to build a Knowledge Graph to allow businesses to successfully differentiate and compete today? We will demonstrate how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.

Graph-Powered Machine Learning - Slides

28 Mar 2018 slides ML graphs

Graph-Powered machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.

Graph-Powered Machine Learning

Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.

Graph-Powered Machine Learning

28 Mar 2018 videos ML

Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.