During my bachelor's, I picked up Albert-László Barabási's Network Science book for a research project. The premise is elegant: protein interactions, power grids, social media feeds, financial markets, and many other complex systems can be modeled as graphs, with objects as nodes and relationships as edges. Once you have a graph, you can measure things: who is central, what clusters form, how fast information spreads, and whether the network has the "small-world" property where any two nodes are surprisingly close [1].<p>Scientific collaboration networks fell into this framework naturally and captivated me. I wanted to apply these ideas to Brazilian computer science to answer some questions that I had out of curiosity. The result is a peer-reviewed paper, Beyond Boundaries: Collaboration Networks and Research Output in Brazilian Computer Science, co-authored with André Vignatti, published in the XIV Brazilian Workshop on Social Network Analysis and Mining (BraSNAM) [2]. This post is my attempt to bring the findings out of LaTeX and into plain language, while adding some context about the research.