{:.image-caption} Network wormholes in Singapore’s Twitter network, from Park et al, Science 2018. "Each dot represents an individual, and each edge represents a bidirected @mention. Nodes and edges are colored according to membership in distinct network communities. A sample of network wormholes (with range six or above and above-median tie strength) is shown in yellow. The inset highlights a single wormhole of range eight, i.e., the second-shortest path between the yellow nodes requires traversing eight intermediary ties (blue edges). The sizes of the nodes in the inset are proportional to the number of network neighbors."

Why does Linux and the broader open-source ecosystem thrive despite weak economic incentives? Why do complex software systems sometimes fail despite being well-engineered? What makes a social media recommendation algorithm so effective and so toxic at the same time? Why do YouTube mega-influencers with tens of millions of subscribers exist, yet each of us can only recognize a handful at best? How do echo chambers and polarization emerge in social media platforms? How can you land your dream jobs? How does mass adoption of technological innovations happen? Underlying these seemingly unrelated questions is the powerful influence of social networks, the collection of on- and offline connections and dependencies that people and systems form with one another, often unknowingly.

This course offers an introduction to the study of social networks and builds the skills needed to answer these wide range of questions by interweaving two threads. First, we introduce network science concepts and their mathematical and graph theoretical foundations, to give rigorous definitions to fuzzy words we use to describe the social world, such as "status" and "social group." Second, we apply these network concepts hands-on, to statistically model and study a wide range of puzzling online social phenomena in real-world networks.

After completing this course, you will be able to:

• construct an adequate social network representation of a given social domain
• proficiently analyze network data, and
• interpret the results with socially meaningful insights

• Lectures: Tuesdays & Thursdays 2:00-3:20pm, Eastern Time, in PH A18B
• Assignments, private announcements, reading materials: Canvas
• Slides: this website
• Instructors: Patrick Park & Bogdan Vasilescu
• Teaching assistant: Meryl Ye

The syllabus covers course overview and objectives, evaluation, time management, late work policy, and collaboration policy.

Below is a preliminary schedule for Fall 2024. The schedule is subject to change and will be updated as the semester progresses. For previous schedules, see Fall 2023.