Graph Machine Learning meets UX; an uncharted love affair
By: Nhung Nguyen
Tags: Machine Learning, Data Visualization, UX, Data Science, UX Design

Designed using resources from Freepik.com and The Noun Project

I am a User Experience (UX) designer. Folks like me look into all aspects of the end-user’s interaction with a company, its services, its product. What drives us is designing meaningful products that are intuitive, meet user needs, and delight.

While I have these drivers, I’m certainly not playing in a space well-paved by other UX designers.

Day to day I work with a bunch of incredible humans who build machine learning algorithms for large-scale graph networks. We’re also building a visualisation tool so people can see the hidden insights from these models and make data-informed decisions in often high-risk scenarios.

For me, it’s fast, exciting, fascinating, uncharted, difficult. A steep learning curve? Absolutely. But compounding that, I’ve found very few case studies of other UX designers working with machine learning.

For others like me who may be equally as bedazzled by this rapidly emerging technology, and just as drawn to its budding relationship with UX; this article is for you!

We’ll first explore what machine learning on graphs is, then build an end-to-end story to define the users, their relationships, and their different needs. I’ll also share three key challenges I faced in the task of designing visualisation solutions for graph machine learning, but how returning to UX design 101 resulted in an accessible, purposeful outcome.

Building the scenario

I quickly discovered that once you learn about graphs, you’ll start to see them everywhere. Many real-world datasets can be naturally represented as networks or graphs, with nodes representing entities and links representing relationships or interactions between them.

My work — represented as a graph

As for managing the vast, ever-increasing amount of connected data being generated in this data-driven age, this is when machine learning comes into play. Machine learning on graphs can draw powerful insights from connected data, like predicting relationships between entities that are missing from the data or resolving unknown entities across different networks (read ‘Knowing Your Neighbours; Machine Learning on Graphs’ to delve deeper).

My job as a UX designer is to question what problems our users can solve with graph machine learning. Graphs have proven to be well suited to crime investigation, and the potential of using machine learning on graphs for investigative analytics in law enforcement is immense. The trouble is, understandable security restrictions in this domain make access to data impossible.

In the absence of data, it’s difficult to develop a tool that can analyse or visualise it. When products are developed by technology first, they risk failing to deliver on what the user actually needs. It can also be difficult for teams to establish a meaningful direction, ending up with something like this: “Let’s cater for all types of tasks, data and scenarios…!”

In mapping our product we ended up with a long list of features on a roadmap, which is a great start. But we needed something to connect these features in a meaningful way to demonstrate the value of the product and help us prioritise. We needed to build a scenario.

By scenario, I mean an end-to-end story of users with a data challenge, how they might use our machine learning on graph algorithms to solve that challenge, and how they could then use the visualisation tool to leverage further insight.

We found an open source Twitter dataset containing 100k users, and 20m tweets. A small portion of the dataset had users labelled as ‘hateful’ or ‘normal’ (not hateful). (Read ‘Characterizing and Detecting Hateful Users on Twitter’ for more detail about how these labels were created, and an explanation of labelled versus non-labelled data for supervised and unsupervised machine learning approaches).

We asked ourselves: Could machine learning on graphs be used to predict ‘hateful’ users in online social networks?

We used the profiles already labelled as hateful or normal as training data for the machine learning models before running our algorithms on the dataset. The results were promising, demonstrating that the model could indeed predict hateful Twitter users (see ‘Can graph machine learning identify hate speech in social media networks?’ for the full case study).

As well as this, I had user research from the law enforcement domain outlining two distinct user types: the data scientist, and the intelligence analyst. I also had mapping of how they work together, and the problems they encounter when working with data.

Combined, this information gave me the base ingredients to build a scenario around the features.