Visualizing My LinkedIn Network. Seeing my entire network in one... | by Tavish Gobindram | Towards Data Science

That’s pretty much how I feel at networking events in real-life. However, using LinkedIn to build up my network has been a completely different experience — I even landed one of my internships by cold-messaging someone on the platform. So I really value this platform, a lot.
But LinkedIn has failed me in one major aspect, it’s really hard to understand your network. What I mean by this is that LinkedIn doesn’t provide any handy statistics or visual tools to let you understand your LinkedIn-sphere which makes it hard to leverage it to the max.
This is pretty much as good as it gets when it comes to exploring your own LinkedIn network.
You can play around with some filters, but that’s it. You can’t really see your whole network as a glance or any statistical summaries, so it’s hard to derive an understanding of your data.
I feel that visualizing my network, would help me finally understand my LinkedIn footprint — who’s in them, where do they work, is it diverse etc. And until this becomes a feature on their platform, I hope you’d be inspired to be proactive and visualize your own network to finally understand what kind of people make up those 500+ connections in your network.
If I want to visualize my network, the first step is to get my network data. You might think that there’s an API for this, and you’d be right. But LinkedIn’s API is not so straightforward to use — multiple permissions are needed to access connection data and for good reason. But all I need to visualize my network is my data. And LinkedIn is nice enough to make the process of downloading your own data fairly simple — no APIs necessary, just download a good old csv file.
So now that the data is available, I need to start analyzing the hell out of it — my go-to tool for this is the trusty Jupyter Notebook.
Here’s the first few rows from the Connections.csv file I downloaded.
import pandas as pddf = pd.read_csv('Connections.csv')df.head()
It lists the emails, current positions and companies of everyone in my network and when they became a part of my network. But printing rows of a csv is clearly not the best way to visualize or understand my network.
To visualize my entire network in one glance, I’ll be using Plot.ly’s Tree Map.
import plotly.express as pxpx.treemap(df, path=['My Network', 'Company', 'Position'], width=1200, height=1200)
Lets take a closer look ...
I only found out about Tree Maps very recently, and I am so glad I found them as it captures LinkedIn network data so elegantly.
The plot categorizes my network into a hierarchical structure — network, companies in my network and positions in those companies. The size of each tile indicates how many people belong to that group. So the larger the company tile, the larger percentage of my network it occupies. And same goes for the tiles within companies. This made it so easy and fun for me to explore my network composition.
But I was not done just yet. By switching a couple of parameters around in the code, I can explore a position-centric view of my network instead of the previously visualized company-centric view.
px.treemap(df, path=['My Network', 'Position', 'Company'], width=1200, height=1200)
And we can take a closer look yet again.
After playing around with these visualizations for way too long, I started thinking about what they were saying.
Comparing the company and position-centric tree maps gave me an interesting insight. In the company focus, I can see that there are clear dominating companies, such as JP Morgan, HKUST and Goldman Sachs. But the position focus, shows a more democratic view. A few dominating positions are present: Software Engineer, Analyst and Associate — but the big picture shows a more diverse presence (more tiny boxes and less clunky boxes). This surprised me as it ran against my initial intuition “There are less types of jobs than there are companies”. Instead, there are many more niche roles than I anticipated and they overpower the diversity of companies (in my network, at least).
Individually, these tree maps also made me realize that my LinkedIn networks were much more diverse than I had thought. Looking at my recommendations and people I recently added, I often think that some companies and definitely some professions make up a majority of my network. But this was far from the truth, because I now realize that most of my network is made up of companies and professions which contain just one or two people.
Essentially the findings were a ...
You can modify the code and interact with the visualizations in full-force by going to the Binder environment right here.
You can also access the code base over here.