I make a specific claim about myself: that I sit between worlds that do not normally talk to each other. So I treated my own network of 1,606 people as a dataset and let the math decide. The structure agreed.
Everyone says they "connect people." It is the emptiest line in professional life. I wanted to know if it was actually true of me, in a way I could measure. So I took my own professional network, the same kind of messy data I work with for organizations, and asked the numbers a simple question: do I really sit between groups that would not otherwise meet?
A network is just dots and lines: people, and who knows whom. When you map a real one, it almost never looks like a single blob. It breaks into clusters, little worlds that mostly talk among themselves. The interesting question is who sits in the gaps between those worlds, because that person is where ideas, opportunities, and trust cross over. I wanted to find out if that was me, or if I just liked to say so.
I exported my full professional network: 1,606 connections across 1,182 organizations, plus years of the quieter signals, the messages, reactions, and comments that show which ties are actually alive rather than just collected.
Built the graph in Python with NetworkX. Detected communities with the Louvain algorithm (modularity 0.522, a clean, well-separated structure). Computed all four standard centrality measures, ran structural-hole detection to find the bridges between clusters, scored tie strength from 890 messages, 1,679 reactions, and 333 comments, and trained a random-forest model to predict likely future connections.
The math split my network into eleven communities, and they map almost exactly onto the chapters of my life: an analytics and marketing world, a tech and startup world, two universities, healthcare, finance, a faith and mission world, and a small but real East Africa cluster.
What this shows: these are not vague labels I assigned. The clustering algorithm found these groups on its own, from who connects to whom. My network genuinely contains separate professional worlds.
Here is the part that mattered. The analytics world and the faith-and-mission world barely touch each other. The American professional world and the East Africa world barely touch each other. The thing joining them is me. The analysis found 35 structural holes, gaps between clusters that are bridged by very few people, and the biggest ones all run straight through the center.
What this shows: notice that the analytics world on the left has no line to the faith-and-mission world on the right except through the center. Same for East Africa. That empty space between clusters is the whole point. Standing in it is what lets me translate one world to another, which is exactly the work I do.
The network did not build evenly. It came in waves, each one tied to a real chapter, with the largest jump in my Notre Dame year.
What this shows: the spike to 502 new connections in 2024 lines up with graduate school. Growth is not random; it follows the rooms you walk into.
"Bridge between data and mission, between America and East Africa" is not a line I wrote to sound interesting. It is the measurable shape of my own network. That position, sitting in the gap where two worlds almost meet, is where translation happens, and translation is the job: turning a number into something a person in a different world can feel and act on.
I did not have to claim I connect worlds. The network already shows it.
This is one person's network, and the raw connection list is only as accurate as the platform that stores it. In any single-person network, some individual scores are partly mechanical, so I lean on the parts that are robust: the cluster structure and the gaps between clusters, which are real and repeatable. The point was never to crown the most important contact. It was to see the shape, and the shape is a bridge.