Visualizing Your LinkedIn Network

In the last couple of years I have made a concerted effort to developed a LinkedIn presence. I can’t quantify the impact, although I have been contacted by a few recruiters who have complimented me on my profile.

Network view

The network visualization tool is fascinating! I enjoyed drilling down into the graphic and seeing networked threads, and the network metrics are useful in evaluating and planning.

Image of LinkedIn network connections

My LinkedIn network

My links have some significant groupings. The largest group of links and interlinks belongs to the categories of software and software developers. I have a significant subset of links with publishers, graphic designers, and instructional designers.

Interesting tidbit

image of three linked circles

Family links

I found a small linkage where I have my brother-in-law (OPP) connected to one niece in banking, and another niece in property management. I’ve only had professional interactions with the OPP, with the other two strictly personal links.

Network metrics

The chart and network metrics tell an interesting story, even if the Socilab network metrics results are for educational and entertainment purposes only.

Network sizes

The results include two sizes for absolute and effective size of my network. The day I captured the data, I had 219 contacts, with an effective size of 184.3 contacts. The difference lies in the uniqueness of information, with the effective size estimating the unique number of clusters I’m connected to.

Takeaway: With an effective size of 33.19%, there’s plenty of room for me to extend my reach, and I should look for further contacts.

Network constraint

The network constraint index measures the focus of my network on a continuum with 1 as open and 100 extremely closed. As I am scattered across a wide range of sectors, my value of 1.28/100 means I am spread across social groups. I must decide whether it’s more important to remain disconnected (which exposes me to new information,) or to attempt to increase the level of connection in my industry.

Takeaway: Looking at the network, I can identify several connection clusters, which strongly correlate with my regular interactions. I’m comfortable with this metric as is.


My chart shows a density value of 35.98%, which means that about 36% of my contacts know each other. The value is derived from the number of ties between my contacts/total number of ties.

Takeaway: If I attempt to increase my contacts, I may see a slight increase in the density.


The higher the hierarchical value, the higher the dependency on a few focal contacts. My value of 40.17% shows that I have a limited dependence on focal contacts, although I identified a few. For example, my literary agent shown in the next image links me to other literary and publishing agents in my network.

Takeaway: Increasing my contacts will likely increase my hierarchical value as well, since I’m looking within the same industries and categories.

Image of segment of network chart showing multiple links

Publishing hierarchy and links

Structural holes

The betweenness raw number for my chart is 22027.9, which is the number of bridging opportunities (structural holes) to which I have access. This is such an interesting value as it illustrates links between two people with me on the shortest path between those contacts.

Takeaway: Increasing my overall contacts has no effect on this betweenness value unless I make an effort to introduce pairs of people linked to me, which I’m not planning to do.

This is a copy of a post made for my course, Digital Learning Environments, Networks, Communities, part of my Master’s program in Learning and Technology at Royal Roads University, Victoria, BC.

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