• Network analysis uses visual representations that make insights more obvious and intuitive.
• Comparing nodes allows you to analyze anything from air travel to social connections at a glance.
• Using different focus metrics, the same flat data set can reveal completely different and valuable insights each time you revisit the network.

Gathering data can be a big project, but analyzing presents an endless sea of choices. It’s hard to know if you’re making the most of your data, but the best way to move forward is to understand your options.

When it comes to efficient and intuitive analysis, a good AI-powered platform and strong visual presentation offer the most. As for visualization, network analysis provides one of the fastest ways to gain deep insights that are often invisible in flat datasets.

What Is Network Analysis?

Across industries, network analysis could mean a number of things, but in the world of big data, it refers to a specific visual representation method that helps make insights more intuitive.

Specifically, a network refers to a structure that represents a group of things (or people) and the relationships between them. This structure is made from nodes and edges. The nodes are the objects of study, and the edges show the relationships between them.

If you have ever seen a detective show (or that one famous meme), a network analysis looks similar to a board with pins and strings. But using modern technology, you have freedom in the presentation that allows you to look at nodes and relationships in a number of ways very easily.

Here’s an example.

Look at a list of flights in the United States (not a map). The list of departers and arrivals can be represented as flat data: just a 2-D table of information.

Now, look at a map of flights in the United States. Both things represent the same information, but with the map, you can see that the airports become the nodes and the edges become the flights themselves.

Uncovering Relationships in Flat Data

Back to Airports

Let’s stick with the first example. Consider again the map that shows all of the different flights across the country.

At a glance, you know which airports have the most flights, and you can see interconnecting flights between airports. Add some weather data to that map, and now you can quickly see where delays are more likely and how they will impact other flights across the country.

With this same representation, you can shift the nodes and edges to look at different aspects of flights around the country. You can make the nodes specific flight crews, individual aircraft, destinations in different seasons, and so much more.

Threat Analysis

Getting into different territory, network graphs can easily highlight two things within threat analysis: the most consequential vulnerabilities and the vulnerabilities with the most paths to be attacked.

Whether you’re looking at hard or virtual threats, the graph shows you where a point of failure impacts other nodes. It also shows you where you have the most access to a node that needs securing.

Infrastructure

Taking that same idea, you can create a network representation of infrastructure. In this case, you might think about the edges first. They might represent power lines, water flow, or internet cables.

The nodes emerge as meaningful intersections depending on what you’re trying to measure and analyze. Again, the most significant areas quickly emerge, allowing you to think about bottlenecks and failure points efficiently.

Logistics

Logistical analysis benefits in similar ways. You can network routes, materials, warehouses, or any other aspect that needs scrutiny. You can see the major resource flow paths, important hubs, and interconnectivity at a glance.

Hidden Revelations

Geography

Let’s return to flights again to explore some less overt insights. Using an airport network map, you expect to see which hubs are busy and how disruption at one hub can influence traffic at the other hubs.

From a different perspective, you can also analyze traffic from minor airports. You might see that a small, local airport favors traffic to an unexpected major airport. Administrators planning flights can see trends like this easily, and it helps with resource allocation and scheduling.

This stems from still looking at the same initial data set and simply shifting focus to a different part of the network. Add in the ability to network different variables, and the potential benefits from your analytics grow quickly.

Social Connections

A common application of network analysis is the exploration of social connections and relationships.

Sometimes, it’s easy to see who is influential in a group, but in many cases, that’s not always so obvious. When you measure social connections across specific metrics, insights may jump off the screen.

Using a military example, you can compare performance metrics across squads, platoons, companies, etc. Using a social network graph, you can see how groups perform and the social connections they have in common.

This helps identify individuals who have the most positive or negative influence over performance. They might be in leadership positions, but the network graph lets you find less obvious influencers just as easily.

Once you find these people, you can zoom in on them to look for behavioral patterns. If the patterns lead to positive outcomes, emulate them. If they lead to negative outcomes, look for ways to address them.

Looking at social connections shows you more than just who knows who. It can help you find positive and negative behaviors that inform your structure, training, assignments, and more.