Security News > 2020 > May > Open source algorithms for network graph analysis help discover patterns in data

StellarGraph has launched a series of new algorithms for network graph analysis to help discover patterns in data, work with larger data sets and speed up performance while reducing memory usage.

One of the challenges data scientists face when dealing with connected data is how to understand relationships between entities, as opposed to looking at data in silos, to provide a much deeper understanding of the problem.

"Capturing data as a network graph enables organizations to understand the full context of problems they're trying to solve - whether that be law enforcement, understanding genetic diseases or fraud detection. We've developed a powerful, intuitive graph machine learning library for data scientists-one that makes the latest research accessible to solve data-driven problems across many industry sectors."

The version 1.0 release by the team at CSIRO's Data61 delivers three new algorithms into the library, supporting graph classification and spatio-temporal data, in addition to a new graph data structure that results in significantly lower memory usage and better performance.

Testing of the new graph classification algorithms included experimenting with training graph neural networks to predict the chemical properties of molecules, advances which could show promise in enabling data scientists and researchers to locate antiviral molecules to fight infections, like COVID-19.

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