Algorithms of Belonging

Picture my life as a graph: nodes are cities (Lahore, Boston, Lubbock), mentors, and passion pivots; edges carry weights—cost, courage, chance. At first glance the network looks random, but graph theory reveals patterns of belonging.

Shortest paths aren't always cheapest.

Classic algorithms minimize summed edge weights. Yet moving straight from Lahore to a U.S. PhD would have been "shorter" academically but heavier emotionally. I detoured through ESL Boston to lower the cognitive load edge-weight of cultural shock—a pruned path rather than shortest.

Centrality predicts influence.

An unexpected high-betweenness node is Adeel Syed, my mentor at SHARE Mobility. Remove that node (he left; I resigned), and network flow reroutes painfully. My resignation wasn't just career drama; it was a structural hole appearing in my personal graph.

Clustering coefficients explain comfort zones.

Lahore friends heavily inter-connect; Boston salsa crew cliques around shared hobbies. Jumping clusters boosts creativity but increases traversal cost. Counting all-pairs shortest paths in large networks is now computationally cheap thanks to new algorithms—metaphorically, journaling helps me compute emotional paths faster.

Edge rewiring fuels growth.

Accepting Texas Tech shifts my hub nearer to AI research in low-resource NLP, creating fresh triangles with mentors and cofounders. Algorithms call it rewiring; humans call it belonging.

In graph form, my life isn't a wandering random walk; it's a deliberate traversal toward maximum eigenvector centrality in purpose.