Resources - graphs

Videos, Slides, Case Studies and other GraphAware related resources

New Case Study

Harness connected data to safeguard your community

Hume for Law Enforcement

Harness connected data to safeguard your community

16 Sep 2022 casestudies Hume Law enforcement

Hume provides a single view of intelligence enabling analysts to quickly identify links and patterns of interest in the vast amounts of information they have access to. On a single canvas, analysts quickly find connections between entities, understand geographical and temporal context, and perform advanced network analysis.

Learn more about how Law enforcement agencies are already using Hume to power their intelligence analysis and achieve success.

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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 Manning Book by Alessandro Negro, Chief Scientist at GraphAware

Graph-Powered Machine Learning

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

Fix your microservice architecture using graph analysis

10 Oct 2019 videos microservices graphs analytics

So, for your brand new project, you decided to throw away your monolith and go for microservices. But after a while, you realize things are not going as smoothly as expected ;-)

Hopefully, a graph can help to detect antipatterns, visualize your whole system, and even do cross-service impact analysis.

In this talk, we’ll analyze a microservice system based on Spring Cloud, with jQAssistant and Neo4j. We will see how it can be helpful to answer questions like:

do I have anti-patterns in my microservice architecture ?

which services / applications are impacted when doing a database refactoring ?

is my API documentation / specification up to date ?

how to get an up to date visualization of my whole system ?

and more !

Lean Dependency Management with graphs

12 Feb 2019 slides graphs

Unblocking dependencies benefits any organization that performs work concurrently. Dependencies are connected and modelling them as a graph surfaces those connections quickly, enabling decisions to be taken that promote zero waste and more efficient delivery.