ONLINE SOCIAL NETWORK ANALYTICS
Course Syllabus
ECTS: 10
Period: Summer 2013 (17 July - 14 Aug)
Level: Master
Language of teaching: English
Course type: Summer University
STADS UVA code: 460122U056
Teachers: Professor George M. Giaglis, Efpraxia D. Zamani
COURSE OVERVIEW
The aim of the course is to introduce students to social network analytics (SNA) and their instrumental value for businesses and the society. SNA encompasses techniques and methods for analyzing the constant flow of information over online social networks (e.g. Facebook posts, twitter feeds, foursquare check-ins) aiming to identify, sometimes even in real-time, patterns of information propagation that are of interest to the analyst. The course will provide students with an in-depth understanding of the opportunities, challenges and threats arising by online social media as far as businesses and the society at large are concerned. It will use case-based teaching and discussions to introduce students to the social and ethical issues that often arise by mining the publicly available information across online social networks for business purposes and/or other types of analyses. Finally, students will be introduced to the concepts of the wisdom of the crowds and social learning, investigating the conditions under which opinion convergence (asymptotic learning) or herding may occur in online social networks.
TOPICS
Basic social network concepts (nodes, edges, network visualization); Network centrality, clustering and communities, strong and weak ties;
Information diffusion, contagion and opinion formation in social networks, small-world phenomena;
Collective action, social movements (e.g. grassroots, groundswell), viral marketing; Aggregate behavior, prediction markets, opinion manipulation, herding;
Voting, democracy, autocracy, and decision-making in social networks;
Topics will be covered through a case study approach, where appropriate. Practical applications of SNA will be addressed, although the course does not adopt a predominantly technical/mathematical perspective on subject coverage.
BIBLIOGRAPHY
Course Books
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets. Cambridge University Press: New York (available here).
Jackson, M. O. (2008). Social and Economic Networks: Princeton University Press.
Additional Bibliography (for reference only)
Textbooks
Barabási, A.-L., & Martino, M. (2012). Network Science Retrieved from http://barabasilab.neu.edu/networksciencebook/
Newman, M. E. J. (2009). Networks: an introduction: Oxford University Press.
General audience books
Barabási, A.-L., & Frangos, J. (2002). Linked: is about How Everything is Connected to Everything Else and What It means for Business, Science, and Everyday Life: Perseus Publishing.
Buchanan, M. (2002). Nexus: Small Worlds and the Groundbreaking Science of Networks. New York: W. W. Norton & Company.
Christakis, N. A., & Fowler, J. H. (2009). Connected: The surprising power of our social networks and how they shape our lives: Little, Brown and Company.
Li, C., & Bernoff, J. (2008). Groundswell: Winning in a World Transformed by Social Technologies: Harvard Business Press.
Papers
Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378-382.
Aral, S., & Walker, D. (2011). Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks. Management Science, 57(9), 1623-1639. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in
information diffusion. Paper presented at the 21st international conference on World Wide Web, Lyon, France.
Cardoso, G., & Lamy, C. (2011). Social Networks: Communication and Change. JANUS.NET e-journal of International Relations, 2(1).
Centola, D. (2010). The Spread of Behavior in an Online Social Network Experiment. Science, 329(5996), 1194-1197.
Centola, D., & Macy, M. (2007). Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3), 702-734.
Clauset, A., Shalizi, C., & Newman, M. (2009). Power-Law Distributions in Empirical Data. SIAM Review, 51(4), 661-703
Dodds, P. S., Muhamad, R., & Watts, D. J. (2003). An experimental study of search in global social networks. Science, 301, 827-829.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75-174. Fowler, J. H., & Christakis, N. A. (2010). Cooperative behavior cascades in human social
networks. National Academy of Sciences (PNAS), 107(12), 5334-5338.
Gastner, M. T., & Newman, M. E. J. (2006). The spatial structure of networks. The
Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380.
Hill, A. L., Rand, D. G., Nowak, M. A., & Christakis, N. A. (2010). Emotions as infectious diseases in a large social network: the SISa model. Proceedings of the Royal Society, 277, 3827-3835.
Kearns, M., Suri, S., & Montfort, N. (2006). An Experimental Study of the Coloring Problem on Human Subject Networks. Science, 313(5788), 824-827.
Lazer, D., & Friedman, A. (2007). The Network Structure of Exploration and Exploitation. Administrative Science Quarterly, 52(4), 667-694.
Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27, 1152-1161.
Madhavi, C. V., & Akbar, M. (2011). Groundswell Effect Part I: A New Concept Emerging in the World of Social Networks. Strategic Change: Briefings in Entrepreneurial Finance, 20, 31-46.
Newman, M. E. J. (2006). Modularity and community structure in networks. National Academy of Sciences (PNAS), 103(23), 8577-8582.
Padgett, J. F., & Ansell, C. K. (1993). Robust Action and the Rise of the Medici, 1400-1434. American Journal of Sociology, 98(6), 1259-1319.
Page, L., Brin, S., Motwani, R., & Winograd, T. (1998). The PageRank Citation Ranking: Bringing Order to the Web. Technical report: Stanford University.
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 814-818. Travers, J., & Milgram, S. (1969). An Experimental Study of the Small World Problem.
Sociometry, 32(4).
Watts, D. J., Dodds, P. S., & Newman, M. E. J. (2002). Identity and Search in Social Networks. Science, 296(5571), 1302-1305.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440-442.
COURSE SCHEDULE
In the following, GG and EZ denote the course’s instructors (George Giaglis, Efpraxia Zamani), while EK and MJ denote the course books (Easley & Kleinberg, Matthew Jackson)
Day 1 (July 17, 09:00-13:00, GG): Introduction and Basic Definitions EK Chapters 1 & 2
MJ Chapter 1
Day 2 (July 18, 09:00-13:00, GG): Graph Theory and Social Networks (part 1)
Day 3 (July 19, 09:00-13:00, GG): Graph Theory and Social Networks (part 2) EK Chapters 3, 4 & 5
MJ Chapters 2 & 3
Day 4 (July 22, 09:00-13:00, GG): Random Networks MJ Chapters 4 & 5
Day 5 (July 23, 09:00-13:00, GG): Strategic Network Formation MJ Chapter 6
Day 6 (July 24, 09:00-13:00, GG): Diffusion EK Chapters 19, 20 & 21
MJ Chapter 7
Day 7 (July 25, 09:00-13:00, GG): Learning MJ Chapter 8
Day 8 (July 29, 09:00-13:00, GG): Games & Markets EK Chapters 6, 7 & 8
MJ Chapter 9 & 10
Day 9 (July 31, 09:00-13:00, EZ): Information Networks and the WWW EK Chapters 13, 14 & 15
Day 10 (August 1, 09:00-13:00, EZ): Cascades and Power Laws EK Chapters 16 & 18
Day 11 (August 2, 09:00-13:00, EZ): Institutions and Aggregate Behavior EK Chapters 22 & 23
Day 12 (August 5, 09:00-12:00, EZ): Collective Action and Social Movements EK Chapter 19.6
Using Social Media to Save Lives: HELPVINAYANDSAMEER.ORG – ecch M-319 Jericho TV Show and Direct2Dell, based on Bernoff, J., & Li, C. (2008). Harnessing the
Power of the Oh-So-Social Web. MIT Sloan Management Review, 49(3), 36-42.
The SystemGraph Effect, based on Zamani, E.D., Kasimati, A.E. and Giaglis, G.M. (2012). Response to a PR Crisis in the age of Social Media: a Case Study Approach. In the Proceedings of the International Conference on Contemporary Marketing Issues (ICCMI 2012), Thessaloniki, Greece, 13-15 June.
Day 13 (August 6, 09:00-12:00, EZ): Cool and unusual Social Network Analysis applications
Cases
‘Eek! Study Finds Books Are Getting Scarier’, based on Acerbi, A., Lampos, V., Garnett, P., & Bentley, R. A. (2013). The Expression of Emotions in 20th Century Books. PLoS ONE, 8(3).
‘Twitter Study Shows an Increase in Negative Mood Leading Up to Last Year’s London Riots’ (http://www.socionomics.net/pdf/1209SOC.pdf), based on Lansdall-Welfare, T., Lampos, V., & Cristianini, N. (2012). Effects of the recession on public mood in the UK. Paper presented at the 21st international conference companion on World Wide Web, Lyon, France.
