Project Topics

As part of the requirements of the course, you need to complete a project on a topic of your choice. This page is intended to give you ideas about possible project topics. Additionally, we will be happy to discuss candidate topics with you and provide pointers to the literature.

Suggested Topics

1. Develop and analyze graph models that capture homophily (see, for example, references in Jackson's book).
2. Study different disease propagation models with potentially more complex dynamics (see, for example, Damon Centola's work on networks). Study different vaccination policies (for certain graph structures).
3. See the following two articles for various open questions and topics for possible projects.

   Kleinberg, Jon. "Complex Networks and Decentralized Search Algorithms." Proceedings of the International Congress of Mathematicians, Madrid, Spain, 2006, pp. 1-26. (PDF)

   Mark E. J. Newman's. "The Structure and Function of Complex Networks." SIAM Review 45, no. 2 (2003): 167-256. (PDF)

4. Develop possible extensions of existing Bayesian or non-Bayesian models of learning over networks and provide preliminary analysis. See, for example,

   Golub, Benjamin, and Matthew O. Jackson. "Naïve Learning in Social Networks and the Wisdom of Crowds." American Economic Journal: Microeconomics 2, no. 1 (February 2010): 112-149. (PDF)

   Acemoglu, Daron, Munther A. Dahleh, Ilan Lobel, and Asuman Ozdaglar. "Bayesian Learning in Social Networks." National Bureau of Economic Research Working Paper No. 14040, May 2008. (PDF)

   Acemoglu, Daron, Kostas Bimpikis, and Asuman Ozdaglar. "Communication Information Dynamics in (Endogenous) Social Networks." Working paper, Massachusetts Institute of Technology, 2009. (PDF)

   Acemoglu, Daron, Asuman Ozdaglar, and Ali ParandehGheibi. "Spread of (Mis)Information in Social Networks." Working paper, Massachusetts Institute of Technology, 2009. ( PDF)

5. Develop models of diffusion of ideas or innovations in a social network that connects the game-theoretic notions of network effects together with the richer models of random graphs. See, for example,

   Jackson, Matthew O., and Leeat Yariv. "Diffusion, Strategic Interaction, and Social Structure." In Handbook of Social Economics. Edited by Jess Benhabib, Matthew O. Jackson, and Alberto Bisin. Amsterdam, The Netherlands: North Holland, 2010. ISBN: 9780444531872. (PDF)

6. Develop models of trust or cooperation in social networks that extend the ideas discussed in the course. See, for example,

   Karlan, Dean, Markus Möbius, Tanya Rosenblat, and Adam Szeidl. "Trust and Social Collateral." Quarterly Journal of Economics 124, no. 3 (August 2009): 1307-1361. (PDF)

   Ambrus, Attila, Markus Mőbius, and Adam Szeidl. "Consumption Risk-Sharing in Social Networks." National Bureau of Economic Research Working Paper No. 15719, February 2010. (PDF - 1.7MB)

7. Formulate different approaches to thinking about social capital (definition, application, or analysis thereof). See the Web site for Bowling Alone: The Collapse and Revival of American Community.
8. Design a small scale experiment to be implemented on fellow MIT students on how people interact with their friends or choose strategies in simple games. See, for example,

   Leider, Stephen, Markus Möbius, Tanya Rosenblat, and Quoc-Anh Do. "What Do We Expect from Our Friends?" Journal of the European Economic Association 8, no. 1 (March 2010): 120-138. ( PDF)

9. Analyze dynamics of behavior in strategic form games when agents use simple update rules that extend fictitious play or replicator dynamics studied in class. Alternatively, consider the implications of such update rules when these games are played in groups with meetings governed by a graph structure.

Data Projects

1. For inspiration for potential projects, see the Lada Adamic's course Web site for SI 508, SI 708, CS 608 Networks.

2. For data sets that can be explored, see

   • Mark E. J. Newman's network data set compilation.
   • Last.fm music network.
   • Digg.com network application programming interface (API).
   • Scientific co-authorship networks: Google Scholar, Arxiv, Medline, SPIRES, etc.

The questions that can be asked in data projects can vary. You can analyze properties, such as homophily, degree distribution, other statistical properties of real social networks, or try to use the structure of the underlying graph for solution of a problem (for instance, music suggestion schemes that might emerge from the last.fm network).