Social networks

Term: 
Fall
Credits: 
4.0
ECTS credits: 
8.0
Status: 
Elective
Course Description: 

SOCIAL NETWORKS SYLLABUS

Level:  Doctoral
Course Status:  Mandatory 

Full description: 

General scope
The aim of this course is to give an overview of the key ideas of network science from a social science perspective.  The concept of networks has come to pervade modern society, as we routinely make use of online social networking services, as business gets organized into network forms, and as warfare increasingly targets a loose network of combatants.  Network science is an emerging interdisciplinary field, which aims at explaining such complex phenomena, emerging from simple principles of making links.  Sociological research on the invisible network infrastructures of global finance, the emergence of social movements, or the formation and operation of terrorist groups all demonstrated that a few critical links can lead to dramatic transformations.  This course gives an overview of key research findings in these areas, and it also introduces key methods to record, analyze, and visualize network data.  Students will be given access to diverse datasets for class purposes.

Learning outcomes
Students taking this course should be able to formulate a research project using concepts and methods from network science.  Beyond an awareness of network studies in key sociological areas, students should also be able to apply network methods in their own research fields.

Course requirements and assessment
Evaluation in the course is primarily based on a short research paper that is either based on datasets discussed in class, or small scale data collection by students.  Beyond the research paper students should also prepare an in-class presentation, and participate in class discussions.

Basis of Evaluation:
Research paper: 70%
Class participation: 15%
Presentation: 15%

Course Structure
Each meeting in the semester will feature theoretical pieces and applications along with a presentation of techniques informed by these.  In each of the weeks datasets will be provided to try and test ideas discussed in the writings. 
 
Schedule

1. Introduction: networks all around…
Networks became an important element of contemporary public consciousness.  While it is next to impossible to parse out the extent to which our lives became more networked and the increase in the awareness of networks all around us – it is certain that the science of networks is coming of age.  In this first meeting we discuss the key areas where this new science has something to say, using Barabasi's book as a guide.

Barabási, Albert-László. 2002. Linked: The New Science of Networks. Perseus Publishing. (ch 1-2)

Assignment: Use http://www.visualcomplexity.com. Choose your favorite case of visualization of the 708 projects featured in this gallery, and introduce it in a short presentation.

2. The strength of weak ties and the power of six degrees
Social networks have a surprisingly short average path length – between any two inhabitants on Earth there is a path of at most six steps.  This cohesion of social networks is created by weak ties that can be activated in social search.  We discuss practical concepts (paths, path length, network diameter), and we use these concepts on various social networks.

Barabási, Albert-László. 2002. Linked: The New Science of Networks. Perseus Publishing. (ch 3-4)
Granovetter, M. 1973. "The strength of weak ties." American Journal of Sociology 81:1287-1303.
Watts, Duncan J. 1999. Small Worlds. Princeton University Press. (selection)

Assignment: use your account on any online social networking site (Facebook, LinkedIn, etc) and chart the network of your friends. Try to determine the number of steps your friends need to take to reach each other if you exclude yourself from this network.

3. Power and centrality
Networks are far from being a domain of equality.  Contrary to techno-romanticist expectations about the internet, the distribution of attention on the Web is highly unequal.  Networks of corporations are centered around dominant firms.  Personal networks are centered around sociometric stars.  We discuss measures of network centrality, and use the example of the World Wide Web to see how emergent inequalities shape its macrostructure.

Barabási, Albert-László. 2002. Linked: The New Science of Networks. Perseus Publishing. (ch 7)
Broder, Andrei, et al. 2000. "Graph structure in the Web." Computer Networks 33(1-6):309-320.
Barabasi, Albert-Laszlo et al. 2000. "Scale-free characteristics of random networks: the topology of the world-wide web." Physica A 281:69-70.

Datasets:
        Political blogosphere dataset (cca 1500 blogs connected by hyperlinks)
Assignment: Use the account provided in class for IssueCrawler, and map a network of websites of your interests.  It is especially interesting if you try an area of supposed equality, like humanitarian aid, NGOs, education. Give an account of power inequalities.  You can also use the blogosphere dataset.

4. The wirings of the world system
Arguably the most consequential of all networks is the network infrastructure of the world system.  We discuss the import of a network perspective to understand global inequalities and evolving core-periphery structures.  We will also introduce UCINET VI – a software dedicated to network analysis.  We discuss some basic indices and procedures.  Participants will experiment with datasets on international trade.

Smith, David A., and Douglas R. White. 1992. "Structure and Dynamics of the Global Economy: Network Analysis of International Trade 1965-1980." Social Forces 70(4):857-893.
Ingram, Paul, Jeffrey Robinson, and Marc L. Busch. 2005. "The Intergovernmental Network of World Trade: IGO Connectedness, Governance, and Embeddedness." American Journal of Sociology 111(3):824-58.

Datasets: 
        World trade and financial flows between countries 1960-2000.
        World cities network (headquarters and subsidiaries of global firms)

Assignment: compare two of the datasets in the time series, and test your hypotheses about how international trade has changes with basic indices.  You can compare these findings with the network of global cities.

5. Structural holes and brokers
The power of networks is not only about the presence of links – it is also about the absence of links.  Structural holes are opportunities, and brokers are to profit from them.  Brokerage is a general phenomena that can occur in business networks, friendship circles, but also in the webs of the world system.

Burt, Ronald S. 1995. Structural Holes: The Social Structure of Competition. 
Harvard University Press. (selections)
Burt, Ronald S. 2005. Brokerage and Closure: An Introduction to Social Capital. 
Oxford University Press. (selections)

Datasets: 
        The network of financial flows and profit by industries in the US. 
        Various intra-organizational networks of managers.

Assignment: Measure the extent of brokerage possibilities for industries, and relate this possibility with profits.  You can also use international trade data from the previous class, to relate brokerage with GDP per capita for countries.

6. Strategic moves in multiple games
Social networks are always multiplex: networks of friendship, business, political alliances are woven simultaneously.  This provides interesting strategic opportunities for insightful players.  A key example is the rise of the Medici family in Florence from 1400.  Through this example we introduce methods to deal with multiple networks.  We use blockmodeling techniques to identify key positions in multiple networks.

Padgett, John F., and Christopher K. Ansell. 1993. "Robust Action and the Rise of the Medici, 1400-1434." American Journal of Sociology 98(6):1259-1319.
Leifer, Eric M. 1988. “Interaction Preludes to Role Setting: Exploratory Local Action.” American Sociological Review 53(4):865-878.

Datasets: 
        Florentine families, 1400-1434.

Assignment: test the idea of a unique Medici position in multiple networks of marriage, banking, and political alliances.  Contrast these findings with results from a brokerage analysis based on single networks.

7. Cohesive groups
Birds of a feather flock together – a unique feature of social networks is homophily, the formation of cohesive groups.  Groups were among the first phenomena to be discussed about networks as far back as Georg Simmel and the sociometric school.  We reconsider groups from a contemporary dynamic take in the areas of academic teams and business groups.

Benjamin F. Jones, et al. 2008. "Multi-University Research Teams: Shifting Impact, Geography, and Stratification in Science." Science 322:1259.
Vedres, Balazs, and David Stark. 2010. "Structural Folds: Generative Disruption in Overlapping Groups." American Journal of Sociology 115(4):xxx-xxx.
Kossinets, Gueorgi, and Duncan J. Watts. 2009. "Origins of Homophily in an Evolving Social Network." American Journal of Sociology 115(2):405-450.

Datasets: 
        Co-authorship in Mathematics (cca 400 000 scientists).
        Co-authorship in Network Science (cca 1500 scientists).

Assignment: use the clique percolation method to identify coauthorship teams and discuss the distribution of teams by size.

8. Robust structures: attack and resilience in terrorist networks
Social networks are efficient tools for information search, problem solving, coordination – thus network forms of organization are also useable for covert warfare.  Terrorist groups make good use of the robustness and malleability of network structures.  How can one fight such networks?  We use the example of the 9/11 terorrist cells to identify key features of terrorist network structure. 
Krebs, Valdis. 2002. "Mapping Networks of Terrorist Cells." Connections 24(3): 43-52.
Turk, Austin T. 2004. "Sociology of Terrorism." Annual Review of Sociology 30:271-86.
Alberts, David S, John J. Garstka, and Frederick P. Stein. 2000. Network Centric Warfare. CCRP Publishing. (selections)

Datasets: 
        9/11 terrorists and helpers
        Conceptual network of news on terrorism of two months after 2001-9-11.

Assignment: compare the terrorist network to an organizational network of similar size – and discuss the potential ways of recognizing terrorist networks by just network patterns.

9. Fragile structures: entrepreneurs, coalitions and fragmentation in civic networks
Coalition formation is a key process in network building.  Social movements are themselves networks of civic organizations.  These networks are crucial for social change, yet movement formation is a fragile process that easily fall back to factions and fragmented publics.  This is especially complicated today with the strong presence of transnational networks in movement organization. We discuss chances and the potential avenues of movement formation.

Baldassarri, Delia, and Mario Diani. 2007. "The Integrative Power of Civic Networks." American Journal of Sociology 113(3):735-80.
Anheier, Helmut. 2003. "Movement Development and Organizational Networks: The Role of ‘Signle Members’ in the German Nazi Party, 1925-30." in: Mario Diani, and Doug McAdam (editors): Social Movements and Networks. Oxford: Oxford University Press.

Datasets: 
        Alliances in the Sulukule-disctrict movement.

Assignment: identify the inequalities between local movement networks and the network of NGOs with transnational backing.

10. Millions on main square: hidden dynamics of civic networks
Social movement dynamics is unpredictable – protest activism follows avalanche dynamics, with little action most of the time, punctuated by episodes of massive activism.  We explore the nature of complex system dynamics, and discuss potential tactics of triggering such episodes of activism.

Kim, Hyojoung, and Peter S. Bearman. 1997. "The Structure and Dynamics of Movement Participation." Social Forces 62(1):70-93.
Oliver, Pamela E., and Daniel J. Myers. 2003. "Networks, Diffusion, and Cycles of Collective Action." in: Mario Diani, and Doug McAdam (editors): Social Movements and Networks. Oxford: Oxford University Press.

Assignment: Analyze press accounts on the Ukrainian orange revolution, protests against the Iraq war, and anti-war protest against the Vietnam war.  Identify signs of avalanche processes in the number of participants.

11. Sexual networks
Romantic relationships and dating is primarily a matter of just a dyadic link, but these dyads connect into larger network structures that help us discover implicit rules in dating in tenager school networks, and help in understanding the potential spread of sexually transmitted disease.

Bearman, Peter S. et al. 2004. "Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks." American Journal of Sociology 110(1):44-91.
Liljeros Fredrik et al. "The web of human sexual contacts." Nature 411:907-8.
Holme, Petter et al. 2004. "Structure and Time-Evolution of an Internet Dating Community." Social Networks 26(2):155-174.

12. Linking it all up: the emerging science of networks
In this last class we consider general lessons learned about the importance of network structures, and consider how various disciplines speak to one another.  We consider examples of interesting cross-communication between various disciplines: ecology, engineering, physics, and sociology.

Buchanan, Mark. 2002. Nexus: Small Worlds and the Groundbreaking Science of Networks. W. W. Norton and Company: New York and London. (selections)

Learning Outcomes: 

Students taking this course should be able to formulate a research project using concepts and methods from network science.  Beyond an awareness of network studies in key sociological areas, students should also be able to apply network methods in their own research fields.

Assessment: 

Evaluation in the course is primarily based on a short research paper that is either based on datasets discussed in class, or small scale data collection by students.  Beyond the research paper students should also prepare an in-class presentation, and participate in class discussions.

Basis of Evaluation:
Research paper: 70%
Class participation: 15%
Presentation: 15%