GraphAware Blog - GraphAware

The webinar Graphs in Criminal Intelligence was run by Dan Newland, General Manager of ANZ, GraphAware, who is highly experienced with the implementation of graph solutions in several government organisations in Australia.

We are proud to announce the release of Hume version 2.16. This release introduces several new Hume features, as well as Orchestra features, and new components.

London, Oct 3rd - Redhorse Corporation has become an official reseller of GraphAware Hume, a mission-critical graph analytics solution, for the U.S. Federal Government. Redhorse will leverage its existing consulting expertise with GraphAware Hume to bring this solution to its National Security, Defense, and Intelligence Community customers.

We are proud to announce the release of Hume version 2.15. This release introduces several new Hume features, as well as Orchestra features and Amazon SQS support.

We are proud to announce the release of Hume version 2.14. This release introduces the first iteration of the new federated search functionality and the Hume API. We also made various improvements to Hume Orchestra and Visualisations.

We are proud to announce the 2.13 release of Hume. Advanced Expand has been extended to include much more functionality, so you build a large variety of queries visually. We also boosted the capabilities of Alerts.

Organisations face a dilemma: the data they have gathered bears immense potential, yet leveraging it is a significant challenge. This blog post outlines an approach to solving this problem by combining graph technology with data orchestration.

A couple of days ago, we hosted a Logistics Optimisation webinar. We covered some challenges of Logistics Chains and talked about how graphs, graph technology, and Hume can help you tackle them.

Last week we at GraphAware hosted yet another webinar. This time we talked about Money Laundering and how Hume can help you detect money laundering activities. Don’t worry if you missed it; here is your summary of what we covered.

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.

Visualising your data in a Knowledge Graph can be incredibly helpful when performing data analysis. It has the potential to make patterns easier to identify and analyse, and your analysis, data, and insights can be much more evident and easy to understand if your data is visualised in a graph. Graph-based visualisation tools make such visualisations incredibly easily understandable and intuitive, making communicating and explaining your findings and conclusions to your colleagues and other stakeholders effortless. That is why I’d like to walk you through the three main challenges people are likely to experience when visualising data in a graph,...

We are proud to announce the 2.10 release of Hume. With this release, Comments are introduced to Hume, which lets you add text, images, and links as comments to nodes and relationships. Additionally, Snapshots can now be shared, and Temporary Visualisations can be created and accessed by other users via a URL.

GraphAware is now going through an extremely exciting phase. In 2020, we launched our own amazing product Hume, the only graph-powered insights engine in the world, which helps companies shed light on their complex data and make smart decisions. Since then, we have been rapidly growing in all aspects. Our ambitious plans will need plenty of talented people, who want to have a direct impact on a company’s success. Right now, we are already advertising for multiple vacancies, and the number will only grow in the coming months (check out our career page if you are already intrigued).

GraphAware is proud to announce the 2.9 release of Hume. With this release, Alerts are available in Hume, a feature that greatly simplifies the daily work of analysts, data scientists, investigators, and data-savvy business users. And there is more:

Say you have a graph-based project in mind. Be it research in life sciences, a fraud detection machine, or possibly a recommendation app. Anything that relies heavily on relationships between data points is a good case for a graph.

It has been over 8 years since I’ve written the first lines of code for the GraphAware Neo4j Framework as part of my MSc. thesis. That’s when the name GraphAware, as well as the (then) one-man show Neo4j consulting company was born. It is therefore my bittersweet duty to take you on a small trip down the memory laneand announce that we have decided to discontinue the development and support of the Framework and all of its modules.

GraphAware is proud to announce the 2.8 release of Hume, our graph-powered insights engine. The release adds further unique capabilities to Hume’s knowledge graph visualisation and graph data analysis capabilities. Analysts, data scientists, investigators, and data-savvy business users will profit from these benefits:

GraphAware is proud to announce the 2.7 release of Hume, our graph-powered insights engine. The release significantly enhances Hume’s knowledge graph visualisation and graph data analysis capabilities. Analysts, data scientists, investigators, and data-savvy business users immediately get the following main benefits:

GraphAware is proud to announce the release of Hume 2.6. This new release brings some major updates and exciting new features to our customers. In particular:

Few years ago I met Michal Bachman and some other GraphAware heroes at GraphConnect in San Francisco. In those days I also had my job interview for the company. Surprisingly, for the first time since I started my journey in IT, I didn’t find myself explaining how I could be useful despite not having a strong background in engineering and software development. On the contrary, Michal was interested in all the things I could do because of my non-traditional curriculum, with a PhD in Cultural Anthropology, some experience in JavaScript graph visualisation, and a genuine passion for networks and graphs....

GraphAware Hume helps governments in keeping their countries safe. In this 15-minute video, we demonstrate the use of Hume for contact tracing and smart quarantine in the context of the current coronavirus pandemic. Specifically, we will see how Hume can identify people at risk using actual and potential contact tracing, suggest who should be informed or quarantined, visually explain why someone is at risk, find quarantine offenders, and much more.

It’s been a year since I published the Graph Technology Landscape 2019 post on GraphAware’s blog. I consider this a success story because it got a lot of attention and publicity. The landscape was mentioned many times at different places; it was used by Emil Eifrem in his GraphTour and GraphConnect opening keynotes, it was displayed in conference halls, and I received many, many useful comments and feedback. I was even invited to Rik van Bruggen Graphistania Podcast to talk about it, and the episode was referred to in the Top 5 Neo4j Podcasts of 2019 blog posts as well....

BOSTON, July 23, 2019 /PRNewswire/ – GraphAware, a leading Neo4j ISV and consulting practice, today announced the official launch of its Italian Research and Development entity Graph Aware S.r.l., headquartered in Lecce, Italy.This strategic investment by GraphAware represents a significant expansion as an ISV, with a fast growing development team of thought-leaders in GraphDBs with Neo4j, Natural Language Processing (NLP), Machine Learning (ML) and Artificial Intelligence (AI).Led by Chief Scientist Alessandro Negro and CTO Christophe Willemsen,the Lecce R&D center is the main lab and development center for GraphAware’s flagship software platform Hume- with a dedicated local and remote development team...

Few years ago I decided that one day I would create a Graph Technology Landscape map, which would be useful for everyone who wants to discover the players around graph technologies. I started to collect the companies and products, but my research has never manifested into a proper blog post. Till now. I am happy to announce, that the first version of my landscape is published, I hope we can consider this as a start of a long journey.

BOSTON, MA, Nov. 28th, 2018 – GraphAware, a leading Neo4j consulting practice, today announced the official launch of its US entity GraphAware, Inc., headquartered in Boston, MA. This strategic investment by GraphAware aligns with Neo4j’s own rapid market expansion.

A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way isstill a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amountof data and converting it into a useful source of knowledge for further processing.

For most organisations, data security is extremely important. The topic comes up every single time we are training, consulting,or otherwise engaging in the world of graphs and Neo4j. At the same time, security is very difficult and time-consuming to get rightand the implications of getting it wrong can be serious. In this blog post, we introduce the integration of Spring Securityinto Neo4j which provides important security controls and mechanisms for enterprises and governments that make use of theworld’s most popular graph database.

At GraphAware, we help organisations in a wide range of verticals solve problems with graphs.Once we come across a requirement or use case two or three different times, we typically create an open-source Neo4j extensionthat addresses it. The latest addition to our product portfolio, introduced in this post, is a simple library that automaticallyexpires data from the Neo4j graph database.

Specialist in Neo4j consultancy, training, and software development, Graph Aware Ltd has been selected as one of NeoTechnology’s first UK solution partners, under its newly launched partnership program.

In the first part of this short series aboutrandom graph models, we talked about why they are useful and had a brief look at two of them: Erdos-Renyi graphs andBarabasi-Albert model. In this post, we take a look at the “small world” phenomenon and another network model, namelythe Watts-Strogatz model.

Efficient counting of relationships in Neo4j was the cornerstone of my Master Thesisand the reason the very first GraphAware Frameworkmodule called the Relationship Count Module was born. The improvements in Neo4j 2.1around dense nodes and the addition of getDegree(…) methods on the Node interface made me eager to do some benchmarking around relationship counts again.

When one obtains a graph data from a measurement on a real world network, it is sometimes useful to make comparison witha random graph. Such graph is characterised by certain degree distribution, which you can imagine to be a list of degreesof nodes present in the network. The most interesting distributions have certain functional dependence which allowsone to infer what processes are dominant in formation of the network. The processes consequently characterise therelationships between the nodes.