How Explainable AI Improves Network Graph Analysis

Dr. Sagar Indurkhya

Written by Dr. Sagar Indurkhya

Within an organization’s vast datasets are millions—even billions—of multifaceted relationships and dependencies that can define your business and solve your biggest challenges. Network graphs are a powerful tool for examining such connections between these data points, but as organizational data has grown more complex and robust, generating and accurately analyzing network graphs using traditional methods has become difficult.

Thanks to innovations like Virtualitics’ Network Extractor and Explainable AI (XAI), it’s easier than ever to create and leverage the unparalleled insights that network graphs provide. 

There is Power in Networks

Networks consist of a set of entities and relationships between pairs of entities, and they appear all around us.

These entities, often referred to as nodes, can represent a wide range of things such as individuals, organizations, products, or any other discrete entities of interest. The connections between nodes, known as edges, encompass various types of relationships such as:

  • Social Networks consisting of people and the friendships between them
  • Protein-Protein Interaction Networks consisting of the protein complexes in a cell and the interactions between them
  • The Internet consisting of web pages and the hyperlinks connecting them

The best way to study these networks is through a network graph, which is a framework for representing and analyzing relationships between interconnected entities.

One of the network graphs’ key strengths lies in its flexibility. Unlike traditional tabular data representations, which struggle to surface complex relationships, network graphs excel at depicting the intricate web of connections inherent in many real-world datasets.

However, the inherent complexity of business data and the scale of the networks require advanced analytics and techniques just to generate the right network graph, let alone derive insights from it.

Hiring the multidisciplinary talent needed—from backgrounds such as data science, network science, computer science, and other domain-specific areas depending on the business’ industry—is more difficult than ever. Gartner reports that current demand for tech talent, including those in data science, greatly outstrips supply and this trend will continue until at least 2026.

Fortunately, there are new AI-driven technologies that make it possible for anyone to generate and analyze powerful network graphs.

Network Rotation Gif 4-2024

A New Way to Unlock the Potential of Network Graphs

Virtualitics gives data and business analysts the ability to visualize, explore, and understand relationships based on multiple attributes—no matter their skill level. Key insights are automatically surfaced from these network graphs, ensuring stakeholders don’t miss any connections integral to making the best business decisions. There are two critical features that enable this: our Network Extractor and Network XAI routines.

Let’s take a closer look at how both of these patented innovations work:

Network Extractor

Our Network Extractor methods leverage AI to generate 3D network graphs directly from complex, tabular datasets with either categorical or numerical features. This automated technology requires no coding, enabling analysts to unlock hidden patterns and relationships within data with unprecedented ease and efficiency.

Network XAI

One of the strengths of Virtualitics is our ability to help anyone—regardless of data science skill or background—analyze and find meaning in their data. The way we do this is with our proprietary XAI innovations, which guides data exploration using plain language and transparent explanations of what properties of the data characterize network communities and distinguish them from one another.

So, whether it's exploring the connections between fuel, budget, emissions, and supply chains to optimize manufacturing performance or uncovering the network of interactions between patient symptoms, demographics, genetic markers, and more to find valuable trends across communities, Virtualitics’ Network Extractor and Network XAI help identify influential nodes and surface meaningful insights from data like never before.

Extract Maximum Value From Your Data

Network graphs empower users to get even more competitive advantage out of their data. By leveraging the flexibility of network graphs and the transformative capabilities of Virtualitics’ Network Extractor and Network XAI capabilities, businesses can stay ahead of the curve and make data-driven decisions with confidence. 

You can hear more about using explainable AI to generate and analyze network graphs in my recent presentation to Indiana University. Thank you to the university for the invitation!