Note: This is the website for the Fall 2023 offering. It will be updated for Fall 2024.
{:.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: Hongbo Fang
The syllabus covers course overview and objectives, evaluation, time management, late work policy, and collaboration policy.
Below is a preliminary schedule for Fall 2023. The schedule is subject to change and will be updated as the semester progresses.
Date | Topic | Notes |
---|---|---|
Tue, Aug 29 | Introduction | slides |
Thu, Aug 31 | Intro to graph theory | slides |
Tue, Sep 5 | Random networks | slides |
Thu, Sep 7 | Edges vs social ties | slides |
Tue, Sep 12 | Graph signatures and dynamics of social ties | slides |
Thu, Sep 14 | Homophily and degree correlation (part 1) | slides |
Tue, Sep 19 | Homophily and degree correlation (part 2) | slides |
Thu, Sep 21 | Centrality and power | slides |
Tue, Sep 26 | Centrality and power in social exchange | slides |
Thu, Sep 28 | Detecting communities | slides |
Tue, Oct 3 | Structural equivalence | slides |
Thu, Oct 5 | Connections through affiliation | slides |
Tue, Oct 10 | Exemplary studies | slides |
Thu, Oct 12 | Midterm exam | |
Tue, Oct 24 | Scale-free networks | slides |
Thu, Oct 26 | Network inequality | slides |
Tue, Oct 31 | Small world | slides |
Thu, Nov 2 | Bridging social capital | slides |
Thu, Nov 9 | Bonding social capital | slides |
Tue, Nov 14 | Network analysis of Open Source Software | slides |
Thu, Nov 16 | Visualizing network data | slides |
Tue, Nov 21 | Hands-on network visualization workshop | no slides |
Tue, Nov 28 | Ethical issues | slides |
Thu, Nov 30 | Guest lecture: Clio Andris | slides |
Tue, Dec 5 | Diffusion and contagion | slides |
Thu, Dec 7 | Student project presentations |