Have you ever been contacted about suspected fraudulent activity on a social media platform you use?🀳 When patterns or locations depart from the norm, social media networks alert you. During the modern era, we are surrounded by examples of this every day.

This blog post is all about the Relationship Chart – although its not too good at sticking to just one relationship at a time 😜. Staying in line with its polyamorous spirit, the relationship chart’s tendency to find relationships wherever it looks is actually a very good thing, as it helps us determine which correlations are important, which aren’t, which are strong – or fragile, so that we can design an effective strategy based on these facts, no matter what our agenda is.

Structure

Models of data are represented by nodes and links, and the strength of a connection between two nodes depends on their interrelationship. Consider the following diagram ⬇️. ‘Bob’ is a node. Bob is interested in the Mona Lisa painting, which is located in the Louvre, which, in turn, is a museum.

Source: www.allthingsdistruibuted.com

Graphs of relationships can be quite simple, such as this one above. In Caplenas relationship graph, the concept is the same, but instead of individual people, places or things, Caplena groups responses to surveys or other types of text comments into bulk categories known as codes.

Whats a code?

Let’s say that instead of one sheet of paper with Bob’s name on it, the node of Bob is a stack of 200 sheets, full of people interested in the Mona Lisa painting. Its the same principle, but since there is now a stack of people that were categorized into having the same interests of Bob, it is known in Caplena, as a code.

An example of what codes would be assigned to a text comment.

Applications

Understanding how topics relate to each other can come in handy in a variety of ways. Advanced disease research can employ relationship graphs to display links between diseases and gene interactions. By examining these links, you can identify patterns in protein pathways that may contribute to a particular disease. Mind boggling stuff, isn’t it? A sufficient amount of relationship data will even help you predict the future!

Check out the video below to see how a typical relationship graph would look in Caplena.

Relationship Graph in Caplena

Let’s go through one more example on Caplena using a NPS survey on mobile carriers. When you hover over what makes the Unlimited Data code, it shows a correlation with being Cheap/Affordable. This means that when people to talk about how cheap and affordable the service is, they often also mention that they have unlimited data.

To stay on theme with the NPS survey of mobile carriers above, imagine yourself as the head of the mobile carrier and how helpful these relationships could be to understand what you need to do to improve and where you should invest your resources. You could invest your time & money in a smarter way if you realize that (for example) unhappy customers are often caused by rude customer service – rather than what you first thought was the issue – connectivity.

Chart Features

There are way more features in Caplena when it comes to this graph, though! You can, for instane:

Change the measurment which is used to compute the correlation between two codes…
Set minimum co-occurences to plot link…
…or exclude selection of codes!

Would you like to check out the correlation chart for yourself? We can walk you through the application, including this, if you book a demo or directly start a free trial. Just follow our in-app wizard and you’ll have your first visualisation in no time!

Did you enjoy this article? πŸš€ Feel free to suggest more topics and we will do our best to write about it!

βœ‰οΈJust email our Head of Marketing sheila [at ] caplena.com

To give Caplena a try for free, click here.

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