Why Graphs?

Reveal hidden insights from your data by connecting the dots

Your Data

The world we know and live in is deeply connected, though sometimes these connections are not always obvious.

 

If you imagine what you are connected to, you might think of your friends, your family, and places you regularly visit. In business this could be your competitors, suppliers, where you operate, who you sell to and who you want to sell to.

 

In many cases these data points come from multiple sources, where on their own they tell only part of the story. Being able to connect all these data points together and articulate the relationships between them gives a richness to the story that enables us to see the whole picture, and in doing so make better decisions.

Modelling Data As A Graph

Today, many technologies capture data in table format – this provides a two-dimensional representation of data, which can be enriched by connecting many rows, tables together.

Capturing your data as a graph extends this capability and enables the rich, relationship-driven structure to be modelled.

The benefits of this approach are:

  • Seeing the whole picture, as data from multiple sources can be modelled in one graph

  • Deeper understanding of the relationships between data points, as directional relationships can be represented

Machine Learning On Graphs

Once you have the whole picture, the next challenge is to determine what in this data is important to inform your data-driven decisions.

 

Machine learning techniques enable us to process the connected data at scale to get:

  • Greater clarity on the contents

  • Reveal hidden connections

  • Prioritise what is important

  • Find with greater speed what we are looking for.

 

At StellarGraph we employ a number of techniques which you can read about in Machine Learning, though here are a couple of the most critical to get you started:

 

Entity resolution is the process of finding out who’s who inside your data. This is a universal problem across data lakes, or any situation where more than one data set is combined. Names, addresses, company names and phone numbers can all be written in slightly different ways and are difficult to match. Graph Entity Resolution uses the rich network data to help uncover the truth, improving the matching accuracy automatically and providing the user clearer visualisation on how they are alike.

Predictive modelling is the process of using the relationships of your data to reveal hidden connections and alert areas of interest for your attention. Predictive models can help improve efficiency when resources are limited, by prioritising items and reducing bias in the decision process of what is of greater risk/opportunity.

Graph predictive models can leverage the full graph relationships to enable predictions that are not currently possible with standard techniques.

Data visualisation, and techniques to enable network data exploration to build trust in machine learning outcomes. Today large datasets when visualised are unintelligible to the human eye, graph machine learning can use a number of techniques such as summarisation, community detection and pattern detection to cluster data that matter and display them in ways that make sense to users.

Use Cases

The applications for this technology exist wherever high value connected datasets exist including health, marketing, supply chains, bioinformatics, law enforcement and cybersecurity.

 

Feel free to try out our software or contact us for more information.

 

CONTACT US

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