The increased focus of companies on data collection and mining has brought an increase in corporate demand for the use of machine learning (ML), as a crucial tool for data consumption and for transforming this data into useful information for companies, a fact that drives the growth of AI in companies.

The newest movement is the emergence of Graph AI, which does not reinvent the Machine Learning wheel, but brings a new way to analyze several different datasets to infer context and investigate relational correspondence, using graphs to facilitate the visualization of these correlations.

What is Graph AI?

Graph AI is a new movement that encapsulates the best of graphics technologies in today's world with Artificial Intelligence (AI).

It enables organizations to complete end-to-end workflows on a single platform as it is powered by a scalable CG engine and eliminates unnecessary data movement.

In short, Graph AI is the science of using ML in graphs to focus on relationships between variables in order to gain deeper insights. When using Graph AI with clustering, partitioning or PageRank algorithms some problems become easier to solve. This happens when issues where centrality, connectivity, and path analysis play a key role in the analysis.

It is worth highlighting here the four pillars of Graph AI, which are: graph query, graph mining, graph analysis and graph AI.

Where does Graph AI get its data?

Anywhere, depending on the application. This can be weather data or transactional data related to specific customers. These can be social networks that can be exploited for social insights. It could be traffic movement, content, scientific data, or IoT. It could be all these things combined and correlated to produce AI based on contextual graphics.

What can Graph AI do for you?

Graph AI's greatest achievement is its predictive and prescriptive capabilities. For example, Graph AI can reveal exactly how a cybersecurity breach occurred and what steps companies can take to prevent further attacks from succeeding.

Another good example includes features to segment and micro-segment customers and their preferences. Netflix, for example, can identify the category of movies that person A likes and those that person B likes to recommend movies to both or a third person they know might like. While this example may seem trivial, it is applicable to issuing precise recommendations for segmenting customers in verticals such as financial services, for example, based on factors such as whether they are in rural or urban areas, or non-obvious segmentations related to consumer spending habits. customers. In the first case, one can employ graphical AI to find commonalities between them for comprehensive marketing campaigns that appeal to both or a third group with residences in both areas.

We can also add that companies like Pinterest, Uber, eBay and Google have reported significant improvements in core areas such as recommendations, fraud detection and prediction models after the incorporation of Graphic Neural Networks (GNNs).

In short, Graph AI can be used in everything.

Final Words

It's only a matter of time before charts become a standard method of analyzing data for operational insights.

There are several reasons why Graph AI is set to grow in importance in the near future. First, companies are increasingly ready: as we noted above, operational data and event data are ubiquitous, and companies already have systems in place to collect and store it. Likewise, as already presented, computing is now also more accessible than ever before.

Second, companies and researchers are reporting that Graph AI is a significantly superior AI technique for their use cases. Teams are demonstrating that graphs can provide better answers to behavioral questions, as well as provide the foundation for smarter fraud detection and recommendation systems.

Ultimately, as we've noted throughout this post, early adopters of Graph AI will have an advantage in their markets and will be able to benefit more quickly from new tools.