Sociology 544: Social Network Analysis

This six-week seminar will provide graduate students an introduction to social network analysis. Obviously in such a short amount of time we cannot cover all of the relevant material, but I have chosen to focus on area that will provide a foundation for future research. The readings are listed below, and I will do my best to fill in the gaps during class discussion. Also, I expect that the readings will evolve some based on student interest. There are no official prerequisites for the course and students from other departments are welcome.

Now a little about mechanics. Each three hour class will consist of a general discussion based on several readings Then, students will take turns presenting specific papers that apply the topics from the general discussion. Students are expected to come to class prepared for the general discussion as well as present a few articles during the course of the semester. There will be no exam, but students will be expected to complete a final paper or project.

Your grade will be based on the following components:

• Class participation and in-class presentations: 25%

Each student will be expected to present a few articles during the course of the year. Each presentation should begin with a 30-second summary of the article, and then move to a more elaborate discussion of the key issues in the paper. The student presenter will be expected to answer any questions that come up from the class.

• Response papers: 25%

Each student will be expected to write two short response papers (2-3 pages) that address the readings of the week. Students should view them as a chance to play with the ideas in the readings: look for contradictions, establish connections to your own research, develop empirical tests, etc. The response papers should not be simple summaries of the readings. Students can choose the two weeks to which they would like to respond, and all papers should be sent to me by Monday at midnight on the day preceding the lecture.

• Final project: 50%

Each student will do some kind of final project. Since the class is only six weeks, these projects will obviously be somewhat limited in scope. One natural project would be to attempt to reproduce the analysis of a paper that you really like as this can often lead to new ideas and new papers. Many of you already have research interests, and you should view these papers as a chance to further develop those interests. We will talk more about the final projects in class. A one paragraph draft proposal will be due the third week of class (10/5/10).

Introduction and the small-world problem (9/21/10)

In this first class we will start with some introductory reading and then learn about the work done on the so-called "small-world" problem. This problem is a nice way to begin the course because it touches on many themes we will revise throughout, and it is one of the few problems in network analysis that has had a sustained combination of empirical and theoretic work.

For general discussion

• Newman, M.E.J. (2010). Networks: An Introduction (Chapter 1) [available from blackboard].
• Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Chapter 1) [available from blackboard].
• Milgram, S. (1967). The small world problem. Psychology Today, 1:62-67 [Available from blackboard].
• Travers, J. and Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32(4):425-443.
• Watts, D.J. and Strogatz, S.H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684):409-410.
• Watts, D.J. (1999). Networks, dynamics, and the small world phenomenon. American Journal of Sociology, 105(2):493-527.
• Kleinberg, J. (2000). Navigation in a small world. Nature, 406:845.
• Watts, D.J., Dodds, P.S., and Newman, M.E.J. (2002). Identity and search in social networks. Science, 296:1302-1305.
• Kleinfeld, J.S. (2002). The small world problem. Society, 39(2):61-66 [Available from blackboard].
• Granovetter, M. (2003). Ignorance, knowledge, and outcomes in a small world. Science, 301:773-774.
• Dodds, P.S., Muhamad, R., and Watts, D.J. (2003). An experimental study of search in a global social networks. Science, 301:827-829.

For student presentation

• Grannis, R. (2010). Six Degrees of "Who Cares"? American Journal of Sociology, 115(4):991-1017
• Korte C. and Milgram, S. (1970). Acquaintance networks between racial groups: Application of the small world method. Journal of Personality and Social Psychology, 15(2):101-108.
• Kogut, B. and Walker, G. (2001). The Small World of Germany and the Durability of National Networks American Sociological Review 66(3):317-335.
• Goel, S., Muhamad, R., and Watts, D.J. (2009). Social Search in Small-World Experiments WWW 2009 701-710.
• Lee, N.H. (1969). The Search for an Abortionist, Chicago:University of Chicago Press: Chapters 1 and 5 [available from blackboard].

Describing network structure, global level (9/28/10)

For the next two weeks, we will review common measures of network structure at both the global level and the local level.

For general discussion

• Butts, C.T. (2009). Revisiting the Foundations of Network Analysis Science 325:414-416.
• Newman, M.E.J. (2010). Networks: An Introduction (Chapter 12.0 - 12.5 and 12.8 [Random Graphs]) [available from blackboard] (SKIM).
• Erdos-Reyni random graph animation
• Barabasi, A.L. and Albert, R. (1999). The emergence of scaling in random networks. Science, 286:509-512.
• Barabasi-Albert random graph animation
• Newman, M.E.J. (2010). Networks: An Introduction (Chapter 13.2 [Random Graphs with General Degree Distributions]) [available from blackboard]. (SKIM)
• Newman, M.E.J., and Girvan, M. (2004). Finding and evaluating community structure in networks. Phys. Rev. E 69:026113.
• Freeman, L.C. (2005). Visualizing Social Networks. Journal of Social Structure.
• Robins, G., Pattison, P., Kalisha, Y., and Lushea, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks 29(2)173-191.

For student presentation

• Liljeros, F. et al. (2001). The web of human sexual contacts. Nature, 411:907-908 with comment and rejoinder.
• Goldberg, A. (2010). Mapping Shared Understandings Using Relational Class Analysis: The Case of the Cultural Omnivore Reexamined. American Journal of Sociology (in press) [Available from blackboard].
• Good, B.H., de Montjoye, Y.A., and Clauset, A. (2010). Performance of modularity maximization in practical contexts. Physical Review E (81):046106.
• Snijders, T.A.B, Pattison, P.E., Robins, G.L., and Handcock, M.S. (2006). New Specifications for Exponential Random Graph Models. Sociological Methodology.
• Goodreau, S.M., Kitts, J.A., and Morris, M. (2009). Birds of a Feather, Or Friend of a Friend?: Using Exponential Random Graph Models to Investigate Adolescent Social Networks Demography 46(1):103-125.
• McGrath, C., Blythe, J., Krackhardt, D. (1997) The effect of spatial arrangement on judgments and errors in interpreting graphs Social Networks 19(3):223-242.
• Baldassarri, D. and Diani, M. (2007). The integrative power of civic networks. American Journal of Sociology, 113(3):735-780.

Describing network structure, local level (10/5/10)

For general discussion

• Milo, R. et al. (2002). Network motifs: Simple building blocks for complex networks. Science, 298:824-827 with comment and reply.
• Freeman, L.C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1:215-239.
• Coleman, J.S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94:S95-S120.
• Burt, R.S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2):349-399.

For student presentation

• Marsden, P.V. (1987). Core Discussion Networks of Americans. American Sociological Review 52(1):122-131.
• McPherson, M., Smith-Lovin, L., and Brashears, M.E. (2006). Social Isolation in America: Changes in Core Discussion Networks over Two Decades. American Sociological Review, 71(3):353-375.
• Fischer, C. (2009). The 2004 GSS Finding of Shrunken Social Networks: An Artifact? American Sociological Review, 74(4):657-669.
• McPherson, M., Smith-Lovin, L., and Brashears, M.E. (2009). Models and Marginals: Using Survey Evidence to Study Social Networks. American Sociological Review, 74(4):670-681.
• Burt, R.S. (1984). Network items and the general social survey. Social Networks, 6(4):293-339.
• Bailey, S. and Marsden, P.V. (1999). Interpretation and interview context: examining the General Social Survey name generator using cognitive methods. Social Networks, 21(3):287-309.
• Marin, A. (2004). Are respondents more likely to list alters with certain characteristics?: Implications for name generator data. Social Networks, 26(4):289-307.
• Bearman, P. and Parigi, P. (2004). Cloning Headless Frogs and Other Important Matters: Conversation Topics and Network Structure. Social Forces, 83(2):535-557.
• Borgatti, S.P. (2005). Centrality and network flow. Social Networks, 27:55-71.
• Baldassarri, D. and Diani, M. (2007). The integrative power of civic networks. American Journal of Sociology, 113(3):735-780.
• Rossman, G., Esparza, N., and Bonacich, P. (2010). I'd Like to Thank the Academy, Team Spillovers, and Network Centrality. American Sociological Review, 75(1):31-51.

Diffusion, spread, and contagion in networks: models (10/12/10)

This week we will review a number of models of how things---both diseases and social behavior---spread and how that spread is affected by the structure of the underlying contact network.

For general discussion

• Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Chapter 21.0-21.6 [Epidemics]) [available from blackboard].
• Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6):1420-1433.
• Watts, D.J. (2002). A simple model of global cascades on random networks. PNAS, 99:5766-5771.
• Watts, D.J. and Dodds, P.S. (2007). Influentials, networks, and public opinion formation Journal of Consumer Research, 34:441-458.
• Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380.
• Centola, D. and Macy, M.W. (2007). Complex contagion and the weakness of long ties. American Journal of Sociology, 113(3):702-734.

For student presentation

• Pastor-Satorras, R. and Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical Review Letters, 86:3200-3203.
• Miller, J.C. (2010). A note on a paper by Erik Volz: SIR dynamics in random networks. Journal of Mathematical Biology, (in pres).
• Doods, P.S. and Watts, D.J. (2004). Universal Behavior in a Generalized Model of Contagion. Physical Review Letters, 92:218701.
• Dodds, P.S., and Watts. D.J. (2005). A generalized model of social and biological contagion. Journal of Theoretical Biology, 232(4):587-604.
• Friedman S.R., Kottiri B.J., Neaigus A, Curtis R, Vermund S.H., Des Jarlais D.C. (2000) Network-related mechanisms may help explain long-term HIV-1 seroprevalence levels that remain high but do not approach population-group saturation. American Journal of Epidemiology, 152(10):913-922.
• Volz, E.M., Frost, S.D.W., Rothenberg, R. and Meyers, L.A. (2010). Epidemiological bridging by injection drug use drives an early HIV epidemic Epidemics, (in press).
• Montanari, A. and Saberi, A. (2009). Convergence to equilibrium in local interaction games. ACM SIGecom Exchanges
• Morris, M., Kretzschmar, M. (1997). Concurrent partnerships and the spread of HIV. AIDS, 11(5):641-648. [see also comment]

Diffusion, spread, and contagion in networks: empirics (10/19/10)

This week we will review attempts to empirically study the spreading of social behavior on networks and the many of the challenges involved. Both experimental and observation studies will be discussed.

• Christakis, N.A. and Fowler, J.H. (2007). The Spread of Obesity in a Large Social Network over 32 Years. New England Journal of Medicine 357:370-379.
• Lyons, D. (2010). The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis. Working paper.
• Nickerson, D.W. (2008). Is voting contagious? Evidence from two field experiments. American Political Science Review, 102(1):49-57.
• Centola, D. (2010). The Spread of Behavior in an Online Social Network Experiment. Science, 329(5996):1194-1197.

For student presentation

• Christakis, N.A. (2010). How social networks predict epidemics. TED Talk.
• Christakis, N.A. and Fowler, J.H. (2010). Social network sensors for early detection of contagious outbreaks. PLoS ONE 5(9): e12948.
• Sacerdote, B. (2001). Peer Effects with Random Assignment: Results for Dartmouth Roommates Quarterly Journal of Economics 11
• Duflo, E. and Saez, E. (2003). The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence From a Randomized Experiment 118(3):815-84
• Hedstrom, P., Sandell, R. and Stern, C. (2000) Mesolevel networks and the diffusion of social movements: The case of the Swedish Social Democratic Party. American Journal of Sociology, 106(1):145-172.
• Coleman, J., Katz, E., and Menzel H. (1957). The diffusion of innovation among physicians. Sociometry, 20(4):253-270.
• Burt, R.S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92(6):1287-1335.
• Van den Bulte, C. and Lilien, G.L. (2001). Medical innovation revisited: Social contagion versus marketing effect. American Journal of Sociology, 106(5):1409-1435.
• Christakis, N.A. and Fowler, J.H. (2008). The collective dynamics of smoking in a large social network. New England Journal of Medicine, 358(21):2249-2258.
• J.T. Cacioppo, J.H. Fowler, and N.A. Christakis, Alone in the Crowd: The Structure and Spread of Loneliness in a Large Social Network. Journal of Personality and Social Psychology 97(6):977-991.
• Fowler, J.H. and Christakis, N.A. (2008). Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. British Medical Journal 337:a2338.
• E. Cohen-Cole and J. M. Fletcher (2008). Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis British Medical Journal 337.
• Cohen-Cole, E. and Fletcher, J.M. (2008). Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic Journal of Health Economics 27(5):1382-1387
• Justin G. Trogdon, James Nonnemaker, Joanne Pais (2008). Peer effects in adolescent overweight. Journal of Health Economics 27(5):1388-1399.
• Fowler, J.H. and Christakis, N.A. (2008). Estimating peer effects on health in social networks: A response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, and Paisstar Journal of Health Economics 27(5):1400-1405.
• Gomez-Rodriguez, M., Leskovec, J., and Krause, A. (2010). Inferring Networks of Diffusion and Influence. KDD.
• Onnela, J.P., Reed-Tschoas, F. (2010). The Spontaneous Emergence of Social Influence in Online Systems PNAS in press.

Networks and time (10/26/10)

Given the growing availability of "digital trace" data, we now have the ability to study how networks change in time, but this also introduces a number of conceptual questions. What if the data we have is not about ties, but about interactions (e.g., email exchanges, conversations, sexual encounters)? What does the dynamics of ties mean for the spreading processes we read about previously? This week we will review some of has been done in this emerging area of research.

For general discussion

• Kossinets, G. and Watts, D.J. (2006). Empirical analysis of an evolving social network. Science, 311:88-90.
• Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 361-395.
• Moody, J. (2002). The importance of relationship timing for diffusion: Indirect connectivity and STD infection risk. Social Forces, 81:25-56.
• Leskovec, J., Kleinberg, J. and Faloutsos, C. (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. KDD, 177-187.

For student presentation

• Berardo, R. and Scholz, J.T. (2010). Self-Organizing Policy Networks: Risk, Partner Selection and Cooperation in Estuaries. American Journal of Political Science, 54:632-649.
• Ingram, P. and Morris, M.W. (2007). Do people mix at mixers? Structure, homophily, and the "life of the party." Administrative Science Quarterly, 52:558-585.
• Snijders, T.A.B., Koskinen, J, and Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. Annals of Applied Statistics 4(2):567-.588.
• Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela,J.P. (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328:876-878.
• Saavedra, S., Reed-Tsochas, F. and Uzzi, B. (2008). Asymmetric Disassembly and Robustness in Declining Networks. PNAS, 105:16466-16471.
• G. Kossinets, J. Kleinberg, and D.J. Watts. (2008). The structure of information pathways in a social communication network. KDD.
• Morris, M., and Kretzschmar, M. (1997). Concurrent Partnerships and the Spread of HIV. AIDS 11:641-648.
• Moody, J., McFarland, D. de Moll, S. (2005) Dynamic Network Visualization. American Journal of Sociology 110(4):1206-1241.
• Onnela, J.P. et al. (2008). Structure and tie strengths in mobile communications networks. PNAS, 104(18):7332-7336.
• Eagle, N., Pentland, A. and Lazer, D. (2009). Inferring Social Network Structure using Mobile Phone Data. PNAS, 106(36):15274-15278 with Comment and Reply.
• Choudhury, M., Hofman, J.M., Mason, W.M., and Watts, D.J. (2010) Inferring Relevant Social Networks from Interpersonal Communication. WWW.
• Bearman, P., Moody, J., Faris, R. (2002). Networks and history. Complexity, 8(1):61-71.