DCS 2350/CSCI 2350:


Social and Economic Networks


Fall 2025


What is this course about?


Networks: A modern way of looking at, reasoning about, and making sense of the world.

Example: What does a Tunisian fruit seller's self-immolation have anything to do with the Saudi-Iran proxy war in Yemen?

Figure 1: A map of the Arab Spring.

Example: Why did Covid impact the northeastern part of the U.S. or countries like Italy so much?

Figure 2: A plot of the number of deaths vs. population density in the U.S. (the Economist).

What do these examples have in common? Additionally, how are they even remotely related to things like the U.S. residency matching or kidney exchange programs?

This course examines the social and economic aspects of today's connected world from a multitude of perspectives; namely, network science, computer science, sociology, and economics. The fundamental questions to be addressed are: What are the properties of real-world networks? What are the effects of networks on our behavioral choices like quitting smoking or eating healthy? How do cascades in networks lead to outcomes like videos going viral? How does Google search the Internet and make money doing so? Debates issues around centrality in networks. Uses game theory to study strategic interactions in networks and markets.

Prerequisites:

What is the central theme?

The focus of this course is networks. Graph theory as a mathematical tool for studying networks has been around for over two centuries. However, modern computational power has enabled the study of the types of networks that not only change dynamically but also scale to a previously unimaginable size. Examples of such networks are today's social and economic networks. This course connects a series of questions within the central theme of social and economic networks. (The specific questions are listed in the course schedule.)


Figure 3: A large-scale network of political blogs. Colors denote political leaning (Adamic and Glance, LinkKDD 2005).

How will these questions be addressed?

A variety of inter-disciplinary tools and techniques will be used to address these questions. For example, we will use a software called Gephi to visualize networks and study their properties. We will study the classical as well as the modern theories of network formation, and we will experiment with these theories in the NetLogo software environment. We will use tools from mathematical sociology as well as computer science to study diffusion in networks. We will get an introduction to computational game theory in order to address various interesting questions on the strategic aspects of networks.

Most importantly, elements of computational thinking will be prevalent throughout this course to answer many of the "how" questions. The reason is that in today's socio-economic context, nearly all real-world networks are very complex with many interdependent components. To answer any conceivable question within such complex settings, we need to think critically about devising a solution. We need to consider computational time and space. We need to combine a mathematical approach with an engineering approach. Computational thinking enables us to do the above in order to devise practical solutions to problems. Programming is just one part of computational thinking; it's not all.

Example: How do we study networks of influence among the U.S. senators?

Figure 4: A nice example of how we address questions on networks is a research project I did with my students Phillips '19 and Ostertag-Hill '20. The figure above gives a bird's eye view of the influence network in the U.S. senate (click on the image to enlarge it). Red nodes are Republicans, blue Democrats, green Independents. Darker nodes have higher threshold and thicker edges have more influence weights. The strongest 40% incoming and outgoing edges for each node are shown. The project used machine learning, social network analysis, computational game theory, and even models from political science.

Why are networks the way they are?

Several critical questions will be addressed in this course. First, almost all real-world networks exhibit some common properties, such as a "giant component" and a certain type of "degree distribution." Why do the entities in the network connect in this fashion? Second, what are the effects of network connectivity on various social and behavioral phenomena, such as smoking, obesity, or even videos going viral? Finally, what roles do strategic interactions play in shaping the networks that we see today?

Instructors

Professor: Mohammad T. Irfan
Email: mirfan@bowdoin.edu
Office Hours:
Tue 2:15-5
Fri 10-12
Room: Mills 209
LAs: Anyi Sun: Tue 7-9pm
Carly Davey: Sun 7-9pm
Room: Mills 105

Time & Place

Lectures: MW 11:40-1:05
Mills 105
Midterm: In-class (10/20)
No final exam

Course Outline

Learning Objectives:

Even after 5 years:

  • Know:
    • How networks are modeled, how they look like in the real world , and how they evolve over time
    • Basics of social network analysis: Small-world phenomenon, clustering, degree distribution, centrality in networks
    • Algorithms like PageRank and web-search auctions running on the Internet
  • Be able to do:
    • Implement network models using computer programs
    • Study the effects of networks, with examples ranging from contagion to smoking and obesity to online videos going viral: How would the effect vary if the network structure were different?
  • Find value in:
    • Networks as an essential DCS tool to better understand today's intricately connected world

What type of course is it?

  • This is a lecture-based course.
  • This is not a projects course. Nor is it a discussion course, even though it will be very much conversational in style.
  • Readings are due after the class, unless otherwise announced.
  • There are some additional notes and video clips that will not be covered in class. However, you are required to utilize these resources to reinforce the key concepts.

Textbooks:


[EK] Networks, Crowds, and Markets,
by Easley and Kleinberg (Required)

[Glad] The Tipping Point,
by Gladwell (Required)

[Jack] Social and Economic Networks,
by Matthew Jackson (Optional)

[Wat] Six Degrees: The Science of a Connected Age,
by Watts (Optional)

[*] Individual chapters from other books (to be provided as hand-outs)

Software:

The following free software will be required. Please install them on your computer.
[1] Gephi

[2] NetLogo 7
You'll most likely install the Mac OS X Silicon version of NetLogo. After you download the dmg file, double click on it and drag-and-drop the NetLogo folder into your Applications folder.

[3] Visual Studio Code with Python 3

Evaluation:

  • 20% Points: One in-class midterm exam

  • 15% Points: Final paper

  • 40% Points: Summative Assessment (SA): Problem solving and programming assignments

  • 20% Points: Formative Assessment (FA): There will be a series of regular formative assignments (FAs).

  • 5% points for good citizenship: Attendance, adherence to the screen policy, and active participation in class, including note taking. Taking handwritten notes is highly recommended for its many benefits. Students will be asked to report the status of their note taking.

  • Points to letter grade conversion: 94% A, 90% A-, 85% B+, 80% B, 75% B-, 70% C+, 65% C, 60% C-, 55% D, below 55% F

Late Policy:

  • Late submissions will be accepted up until 24 hours after the deadline with a penalty. Each hour after the deadline would cost 4% of the points for that assignment. Extensions will be given if there is any health or other emergency situation.

Outline of Topics:

Week Question Topics Readings Additional Notes/Links
Week 1
(9/3)
 Course introduction: Why study networks? What's the foundation of the study of networks? Slides (Course Intro)
Week 2
(9/8, 9/10)
 What is a network? How can we visualize a network? 1. Basics of graph theory
2. Network visualization with Gephi 		[EK] Ch 2 1. Slides (Basics of graph theory)
2. Slides (Gephi)
3. Dolphins dataset for Gephi
Week 3
(9/15, 9/17)
 How can we analyze a real-world network? What are some common properties of networks?
 Macro-level social network analysis:
giant component, small-word effect, clustering, degree distribution
 [EK] Ch 2
[Jack] Ch 2
 1. Slides (Social Network Analysis)
2. 3.5 degrees of separation in Facebook
3. Power law debate
4. Barabasi's response
5. Petter Holme's blog post on it
Week 4
(9/22, 9/24)
 How can we analyze a real-world network? (continued)
Micro-level social network analysis:
1. centrality
2. Experiments with Gephi
Assignments 1 and 2 (experiments and theory) out 		[Jack] Ch 2
 Slides (continued)
Week 5
(9/29, 10/1)
 1. What's the effect of different types of edges?
2. How can we use it to detect communities in a network?
1. The strength of weak ties
2. Community detection algorithms 		[EK] Ch 3
Granovetter's paper
1. Slides (The strength of weak ties)
2. Note on Girvan-Newman algorithm
Week 6
(10/6, 10/8)
 Are the nodes connected to each other kind of the same?
Homophily [EK] Ch 4 Slides: Homophily
Fall Break        
Week 7
(10/15)
 What can we say about a network having friends as well as enemies? Structural balance: positive and negative relationships
[EK] Ch 5
 Slides: Structural balance
Week 8
(10/20, 10/22)
 Modeling Networks: How can we model network formation in the real world? And why should we model?

 1. Network Formation:
Random-graph models and their properties
2. Schelling's model of segregation
Midterm exam on 10/20
 [Jack] Ch 4, 5
[Wat] Ch 3, 4
1. Slides (Models of network formation)
2. Prof. Irfan's NetLogo tool to study Erdos-Renyi random graphs
3. Homophily slides above for Schelling's model
Week 9
(10/27, 10/29)
 Modeling Networks (continued) 1. Watts-Strogatz and preferential-attachment models
2. NetworkX: network programming in Python
Assignment 3 and 4 (modeling networks: theory and experiments) out 		NetworkX tutorial by Prof. Irfan
Week 10
(11/3, 11/5) 		What is the effect of a network as a whole on people's behavior? For example, how does a video get viral on a social network? Similarly, how did the Facebook post "Hi World, we want a puppy!" get over a million likes within a few hours?
Cascading behavior/diffusion in networks
[EK] Ch 19
 1. Slides (diffusion)
2. One person starts a dance party
3. Kleinberg's talk
4. Interview of Duncan Watts by Fast Company
Week 11
(11/10, 11/12) 		How does a disease propagate over a network? What is the difference between the propagation of behavior and the propagation of diseases? 1. Diffusion (continued)
2. Epidemics
Assignment 5 (programming: epidemics and cascading behavior) out 		1. Ch 21 of EK
2. Optional: Ch 10 of Barabasi's Network Science 		Slides (epidemics)
Week 12
(11/17, 11/19)
 We've talked about structures of networks and the connection between network structures and various social phenomena. Can we say something about economic networks where the interactions are primarily strategic? How can we model such strategic interactions? Game theory [EK] Ch 6
 1. Slides (Game theory)
2. A Beautiful Mind: How it missed NE
3. Russia-Ukraine war:
4. Trump & game theory:
Thanksgiving Break        
Week 13
(12/1, 12/3)
 How can we model one very common type of strategic interaction that we see everyday--auctions?
 Auction
[EK] Ch 9
 Slides (Auctions)
Week 14
(12/8, 12/10)
 How does Google and other search engines use auction to make money?
 Sponsored search markets
Assignment 6 (game theory, auction, and sponsored search markets) out 		[EK] Ch 15
 1. Slides (Sponsored search markets)
2. Prof. Irfan's video lecture
3. Greg Taylor (Oxford) on the economics of search engines
Additional topic (time permitting) Matching: Another example of strategic interactions that received many plaudits due to its humanitarian cause is the kidney exchange program. What is the networked economy foundation of such a program?
 The famous stable marriage problem for two-sided matching
 Stable marriage handout
Slides (Matching)
Additional topic (time permitting) Let's talk about one special network that we use everyday--the Internet. What does it look like? How does Google search it? WWW: structure
Link analysis and web search--PageRank
 [EK] Ch 13, 14
 1. Slides (Information Networks)
2. Vannevar Bush's Memex
3. Google's Matt Cutts on PageRank


Collaboration Policy

With some exceptions, summative assignments (SAs) will normally be done individually. In contrast, formative assignments (FAs) will be group work, but you must write your own submission individually.

Students are expected to follow Bowdoin's Academic Honor Code.

Following is the collaboration policy for individual assignments. You are encouraged to discuss ideas and techniques broadly with your classmates, but not specifics of assigned problems. Discussions should be limited to questions that can be asked and answered without using any written medium (e.g., pencil and paper, board, or email). This means that at no time should a student read anything written by another student. Violation of this policy is grounds for me to initiate an action that would be filed with the Dean's office and would come before the Conduct Review Board. If you have any questions about this policy, PLEASE do not hesitate to contact me. This will be a zero-tolerance policy.

If in the future you provide your work to other students, this will also constitute a violation of Bowdoin's honor code.

AI Policy

We will follow the DCS AI policy in this course.

What counts as Generative AI?

Generative AI refers to all the AI systems that can create new content like text, code, images, audio, and other types of media. This includes but is not limited to:

  • Large language models (ChatGPT, Claude, Gemini, etc.), apps and agents derived from them, and aggregator interfaces (Amplify, LibreChat, etc.)
  • Code generation tools (GitHub Copilot, Google Colab, Cursor, etc.)
  • Image generators (DALL-E, Midjourney, etc.)
  • AI-powered writing assistants and autocomplete features
  • NotebookLM, Quizlet AI features, and other AI-enhanced study tools

Always acknowledge what generative AI tools were used to help you complete assignments.

How can generative AI help you to learn in this course?

  • Converting slides and notes to study guides and podcasts (e.g., NotebookLM)
  • Creating practice materials like flashcards and mock quizzes or exams (e.g., Quizlet)
  • Proposing a study plan
  • Providing alternative explanations or examples
  • Suggesting refinements to grammar and corrections to spelling
  • A guide to debugging code

How can generative AI impair your learning in this course?

  • For coding projects, AI often misses design layers, making results hard to debug
  • Reduces critical thinking and problem-solving abilities
  • Can limit creativity
  • Creates a sense of false confidence
  • Creates dependency
  • Reduces retention
  • Limits research skills
  • Denies you the possibility of self-expression in a constructive environment

Other costs of using AI

  • Environmental impact – large energy use
  • Labor exploitation – poorly paid workers for moderation and labeling
  • Digital colonialism – reinforces inequalities and Western perspectives
  • Digital divide – widens gaps in tech access
  • Aggregation – concentrated power in few companies
  • Privacy – inputs may be stored despite disclaimers

Why do we give you assignments?

"We don’t assign essays because the world needs more student essays." - Emily Bender

Assignments are designed to help you develop essential skills and ways of thinking that will serve you throughout your education and beyond. Each assignment is an opportunity to practice critical thinking, develop your unique voice, learn from struggle, engage with material, demonstrate learning, and prepare for future challenges.

Academic Integrity Expectations

  • Be transparent about AI use
  • When in doubt, acknowledge it
  • Submit your prompts with assignments
  • Understand that overreliance hinders learning
  • Violations will be treated as academic dishonesty

When is generative AI not allowed?

  • To generate code. AI code lacks structure and context and is often too complicated to debug (Prof. Irfan can give you many examples). You must write the initial draft of code yourself.
  • To generate text; e.g., to flesh out an outline, leading to an initial draft that you intend to revise
  • To brainstorm – your background is richer than AI’s
  • To solve problems – designed to strengthen critical thinking
  • As a search engine for facts – verify independently
  • To analyze data or interpret results without your own reasoning
  • To generate ideas for creative projects or research questions
  • To complete exams or quizzes unless permitted
  • To translate assignments or materials without permission

When can generative AI be used?

  • Polishing text by checking for grammar, spelling, and styling suggestions
  • Outlining, i.e., preparing an initial structure with AI to be expanded with your own perspective (note that the opposite is not allowed)
  • Debugging your code, including generating test cases
  • Getting help on a software tool (e.g., how do I do X?)
  • Finding resources to support research
  • Testing discussion questions for class
  • Checking your work and getting preliminary feedback

Screen Policy

We will follow the DCS screen policy.

No screen use is permitted in class unless explicitly stated by the instructor or in cases of learning accommodations. This includes phones, tablets, and computers. Most courses will display a symbol during presentations that indicates when screen use is allowed. When screens are not permitted, all devices should be put away and out of sight.

If you have a documented accommodation that allows the use of a laptop or tablet for note-taking, please make sure to submit the appropriate materials to the professor.

If Winter comes, can Spring be far behind? -- Percy Bysshe Shelley