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.
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.