LynxKite strives to make graph analytics easy, fast, and accessible. You can import your data, compute a dozen metrics, visualize your graph, and train machine learning models with a few mouse clicks. But when you have questions that can’t be expressed with clicks, the only option has been to write SQL or Python.

In recent years we saw many things machine learning can do - including meaningful contributions for cancer care. Machine learning algorithms can help detect cancer early, monitor response to therapy, and even understand the underlying cellular processes that led to cancer in the first place.

While 2022 brought much misfortune and tragedy around the world, it was kind to our small project. Our engineering team has doubled in size. We have not just worked off technical debt, but turned it into its opposite. (“Technical credit”?) And we have added some stunning new features in the 5.x versions.

I’m writing this blog post to help our new engineers starting in the summer. It can also be helpful for anyone interested in learning how LynxKite works. Every large application is like a thousand-year-old city: full of clever ideas, some of which became solid pillars while others got covered up with new clever ideas. Put on your archeologist’s hat and let’s dive in!

It feels great to finish a challenging analytics task and see the fruits of your labor on your screen. But the work is not over yet! The next step is commonly one of:

The fun part of graph analytics starts once we have a nice graph to analyze. But to get there we have to first decide how to represent the data as a graph. Picking the right graph model is not trivial even for expert data scientists, not to mention the novice or citizen data scientists who need to get up on a steep learning curve.

Already have graph data in a Neo4j instance? Here is how you can apply LynxKite graph data science magic on it!

A major mobile wallet in Singapore faced challenges to sustain the performance of its customer acquisition campaign. Using LynxKite, data scientists of Lynx Analytics developed a graph-based marketing methodology that identified communities and influencers and created a more personalized and effective campaign.

We show how graph-metric-based targeted intervention can have a huge impact on the course of an epidemic. We are using a simulation on a graph model of a hypothetical infectious disease.

LynxKite provides hundreds of built-in operations from computing the degree of nodes to finding a coloring of the graph and making predictions with a neural network. But what to do if there is no built-in box for what you want? LynxKite has many different options for you to use your custom formulas and code. In this post I review all of them and explain why I would choose one over the other in different situtations.

Between January and April 2020, a LynxKite team (Gabor, Marton and Fai) worked with a group of undergraduate students, Victoria Teo, Ann Qi Neo, Jian Lai Ng and Benita Neo, from The National University of Singapore (NUS) on their Capstone Project. The project requires the students to complete a real-world business analytics project based on the principles they learned from various modules in their business analytics program.

I love reading about how software designs evolve. Finding the best way to represent and communicate things is challenging both in the code and on the user-interface. This article is my recollection of the journey that took us to LynxKite’s current design.

Given a map of a town (a graph, of course!), prospective customers and network access points LynxKite helps us figuring out an optimal fiber network layout.

How can we use Gource and LynxKite to visualize the LynxKite Git commit history in different ways?

[...] Six years and almost 16,000 commits later, today we are extremely proud and excited to announce putting this tool open-source in the hands of the broader community. We are confident that LynxKite can be an important tool in the hands of data scientists around the world and it will help boost the adoption of often neglected graph methods.