Only a few things are more satisfying for a graph data scientist than playing with Neo4j Graph Data Science library algorithms, most probably running them in production and at scale. Possibly also using them to fight against scammers and fraudsters that every day threatens your business.
It is always a valuable opportunity to understand our product better and recognize user needs. At GraphAware, building Hume, a graph-powered insight engine, we are proud of making an impact on our customers’ success. However, we use Hume also to support our processes and help our own needs. In the case of the event that took place throughout December, we were also able to have great fun and integrate the team.
Last week, I had the pleasure of hosting a webinar with our Director of Product, Esther Bergmark, and our CTO, Christophe Willemsen. This webinar introduced our new release Hume 2.11, so we covered the most exciting features of the release, including no-code graph navigation, custom visibility of Actions in Perspectives, and Perspective API. The 2.11 version comes with more updates and advancements such as Configuration as Code and Polygons in Geospatial Analysis - about which you can read in our release blog.
We are proud to announce the 2.11 release of Hume. Advanced Expand is introduced to Hume with this release, which lets users create complex queries to navigate the graph without using Cypher. Additionally, the visibility of Actions can now be defined per Perspectives, enabling a tailored exploration experience; a GraphQL API exposes data from Perspectives so that other apps can leverage the power of Hume. And finally, Configuration-as-Code allows administrators to manage Hume configuration files in a repository.
About a year ago, I first logged into Hume - in the morning, I had started my new job at GraphAware, and a few hours later, I had a canvas in front of me with a few person nodes connected by relationships. Hume Visualisation. The graph: a murder mystery. Me: a newbie, never typed a line of code in any query language.
Graphs are a natural fit for investigative use cases. Whenever you want to analyse a situation containing people, objects, locations, and events (POLE), graphs emphasising relationships between objects are your natural companion.
“Alright, could you please tell us a few benefits and drawbacks of working with microservices?” - starts the usual tech interview question, and the traditional answer often follows: “They’re smaller; therefore, they’re easier to develop and to troubleshoot. There’s better scaling control on parts of the system. They can be written in different programming languages. They can be deployed individually.”
Just a couple of days ago, we hosted a Fraud Detection webinar. We chose to focus on credit card fraud to illustrate how Hume can help detect fraud faster. Our Director of Product, Esther Bergmark walked you through an example of a credit card company investigator looking into suspicious, fraudulent developments. Let me share what we covered at the webinar.
As you know, last week, we hosted an open Q&A event with our very own Dr. Alessandro Negro - the Chief Scientist at GraphAware and author of the book Graph-Powered Machine Learning. In this short blog post, we’d like to share some of the highlights from the event for those of you who missed it.
As you know, our Chief Scientist, Dr. Alessandro Negro, recently published a book titled Graph-Powered Machine Learning. We are very proud of the Chief, and very excited about the book. We're even planning an event, where you'll be able to ask Alessandro anything about it!But what is really in the book? Let me share what the first chapter is about so you know what you're in for - a treat! The book opens with an introduction to Machine Learning and Graphs. The first chapter covers Machine Learning, some of the challenges of Machine Learning, Graphs, and the role Graphs play...