Adventures in Network Science

Every time someone asks me how school is going, I have the tendency to reply with an enthusiastic but nondescript, “AWESOME!” Or, as one of my classmates has taken to saying, “WHAT A TIME TO BE ALIVE!”

Truly, it is a privilege to be able to experience such awe.

As it turns out, however, these superlatives aren’t particularly informative. And while I’ve struggled to express the reasons for my raw enthusiasm in more coherent terms, I will to attempt to do so here.

First, my selected field of study, network science, is uniquely interdisciplinary. I can practically feel you rolling your eyes at that tiredly clichéd turn of phrase – yes, yes, every program in higher education is unique interdisciplinary these days – but, please, bear with me.

I work on a floor with physicists, social scientists, and computer scientists; with people who study group dynamics, disease spreading, communication, machine learning, social structures, neuroscience, and numerous other things I haven’t even discovered yet. Every single person is doing something interesting and cool.

I like to joke that the only thing on my to-do list is to rapidly acquire all of human knowledge.

In the past year, I have taken classes in physics, mathematics, computer science, and social science. I have read books on philosophy, linguistics, social theory, and computational complexity – as well as, of course, some good fiction.

I can now trade nerdy jokes with people from any discipline.

And I’ve been glad to develop this broad and deep knowledge base. In my own work, I am interested in the role of people in their communities. More specifically, I’m looking at deliberation, opinion change, and collective action. That is – we each are a part of many communities, and our interactions with other people in those communities fundamentally shape the policies, institutions, and personalities of those communities.

These topics have been tackled in numerous disciplines, but in disparate efforts which have not sufficiently learned from each other’s progress. Deliberative theory has thought deeply about what good political dialogue looks like; behavioral economics has studied how individual choices result in larger implications and institutions; and computer science has learned how to identify startling patterns in complex datasets. But only network science brings all these elements together; only network science draws on the full richness of this knowledge base to look more deeply at interaction, connection, dynamics, and complexity.

But perhaps the most exciting thing about this program is that it truly allows me to find my own path. I’m not training to replicate some remarkable scholar who already exists – I am learning from many brilliant scholars what valuable contributions I will uniquely be able to make.

Because as much as I have to learn from everyone I meet – we all have something to learn from each other.

There are other programs in data science or network analysis, but this is the only place in the world where I can truly explore the breadth of network science and discover what kind of scholar I want to be.


I joke about trying to acquire all of human knowledge because, of course, I cannot learn everything – no one person can. But we can each cultivate our own rich understanding of the puzzle. And through the shared language of network science, we can share our knowledge, work together, and continue to chip away at understanding the great mysterious of the universe.


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