SOCIOL 208A Reading Schedule (Fall 2024)

Week 1, April 1: Basic SNA Concepts

Readings

  • Prell, C. & Schaefer, D. R. (2023). Introducing Social Network Analysis. In J. McLevey, J. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. Sage Publications. link
  • Light, R. & Moody, J. (2021). Network Basics: Points, Lines, and Positions. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link
  • Harary, F. & Norman, R. Z. (1953). Graph Theory as a Mathematical Model in Social Science. Research Center for Group Dynamics, University of Michigan. link

Other Material

Week 2, April 8: Centrality

Readings

  • Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466-484. link
  • Martin G. Everett & Steve P. Borgatti (2023). “Centrality.” In J. McLevey, J. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. Sage Publications. link
  • Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1(3), 215-239. pdf
  • Agneessens, F., Borgatti, S. P., & Everett, M. G. (2017). Geodesic-based centrality: Unifying the local and the global. Social Networks, 49, 12-26. link

Other Material & Further Reading

  • Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social networks, 44, 153-159. link
  • Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O. (2005). Centrality Indices. In: Brandes, U., Erlebach, T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg (secs. 3.2, 3.3, and 3.4). link
  • Koschützki, D., Lehmann, K.A., Tenfelde-Podehl, D., Zlotowski, O. (2005). Advanced Centrality Concepts. In: Brandes, U., Erlebach, T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg. link
  • Comprehensive list of centrality measures with formulas and software

Week 3, April 15: Ego Networks

Readings

  • Smith, J. A. (2021). The Continued Relevance of Ego Network Data. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link

Week 4, April 22: Status and Prestige

  • Franceschet, M. (2011). PageRank: standing on the shoulders of giants. Communications of the ACM, 54(6), 92-101. link
  • Gleich, D. F. (2015). PageRank beyond the web. SIAM Review, 57(3), 321-363. link
  • Martin, J. L. & Murphy, J. P. (2021). Networks, Status, and Inequality. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link
  • Rossman, G., Esparza, N., & Bonacich, P. (2010). I’d Like To Thank The Academy, Team Spillovers, and Network Centrality. American Sociological Review, 75(1), 31-51. link

Further (Mathy) Reading

  • Vigna, S. (2016). Spectral ranking. Network Science, 4(4), 433-445. pdf
  • Baltz, A., Kliemann, L. (2005). Spectral Analysis. In: Brandes, U., Erlebach, T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg. link
  • Bonacich, P. (1972). Factoring and Weighting Approaches to Status Scores and Clique Identification. Journal of Mathematical Sociology, 2(1), 113-120. pdf
  • Katz, L. (1953). A New Status Index Derived from Sociometric Analysis. Psychometrika, 18(1), 39-43. pdf

Week 5, April 29: Similarity, Roles, and Positions

Readings

  • Burt, R. S. (1976). Positions in networks. Social Forces, 55(1), 93-122. link
  • Breiger, R. L., Boorman, S. A., & Arabie, P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of Mathematical Psychology, 12(3), 328-383. link
  • Lü, L., Jin, C. H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 80(4), 046122. link
  • Jeh, G., & Widom, J. (2002). Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 538-543). link
  • Leicht, E. A., Holme, P., & Newman, M. E. (2006). Vertex similarity in networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 73(2), 026120. link

Further Reading

  • Fouss, F., Pirotte, A., Renders, J. M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on knowledge and data engineering, 19(3), 355-369. link
  • Kovács, B. (2010). A generalized model of relational similarity. Social Networks, 32(3), 197-211. link
  • Liben-Nowell, D., & Kleinberg, J. (2003). The link prediction problem for social networks. In Proceedings of the Twelfth Annual ACM International Conference on Information and Knowledge Management (CIKM’03) (pp. 556-559). link to longer paper
  • Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications, 390(6), 1150-1170. link

Cheat Sheets:

Week 6, May 6: Community Detection

Readings

  • Shai, S., Stanley, N., Granell, C., Taylor, D. & Mucha, P. J. (2021). Case Studies in Network Community Detection. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks Oxford University Press. link
  • Newman, M. E. (2018). Community Structure. In Networks, 2nd Edition. Oxford, Online Edition, Oxford Academic. link
  • Melamed, D. (2015). Communities of classes: A network approach to social mobility. Research in Social Stratification and Mobility, 41, 56-65. link

Further (Substantive) Readings

  • Moody, J., & Mucha, P. J. (2023). Structural Cohesion and Cohesive Groups. In J. McLevey, J. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. Sage Publications. link
  • Shwed, U., & Bearman, P. S. (2010). The temporal structure of scientific consensus formation. American Sociological Review, 75(6), 817-840. link

Further (Mathy) Reading

  • Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 70(6), 066111. link
  • Fortunato, S. (2010). Community Detection in Graphs. Physics Reports, 486(3-5), 75-174. link
  • Girvan, M., & Newman, M. E. (2002). Community Structure in Social and Biological Networks. Proceedings of the National academy of Sciences, 99(12), 7821-7826. link
  • Leicht, E. A., and Newman, M. E. (2008). Community Structure in Directed Networks. Physical Review Letters 100, 118703. link
  • Newman, M. E. (2006). Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582. link
  • Newman, M. E., & Girvan, M. (2003). Mixing patterns and community structure in networks. In Statistical mechanics of complex networks (pp. 66-87). Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Newman, M. E. (2003). Mixing Patterns in Networks. Physical review E 67(2), 026126. link
  • Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. link

Week 7, May 13: Analyzing Two-Mode Networks

Readings

  • Breiger, R. L. (1974). The Duality of Persons and Groups. Social Forces, 53(2), 181–190. link
  • Borgatti, S. P., & Everett, M. G. (1997). Network Analysis of 2-Mode Data. Social Networks, 19(3), 243-269. pdf
  • Everett, M. G., & Borgatti, S. P. (2013). The Dual-Projection Approach for Two-Mode Networks. Social Networks, 35(2), 204-210. link
  • Neal, Z. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance, and other co-behaviors. Social Networks, 39, 84-97. link

Further Reading

  • Borgatti, S., & Halgin, D. (2014). Analyzing affiliation networks. In The SAGE Handbook of Social Network Analysis, First Edition (pp. 417-433), SAGE Publications Ltd. link
  • Faust, K. (1997). Centrality in affiliation networks. Social Networks, 19(2), 157-191. link

Other Material

  • Murphy, Phil, and Brendan Knapp. (2018). Bipartite/two-mode networks in igraph. RPubs https://rpubs.com/pjmurphy/317838
  • Domagalski, R., Neal, Z. P., & Sagan, B. (2021). Backbone: An R package for extracting the backbone of bipartite projections. Plos one, 16(1), e0244363. link
  • Neal, Z. P. (2022). backbone: An R package to extract network backbones. PloS one, 17(5), e0269137. link

Week 8, May 20: No Class (Traveling)

Week 9, May 27: Statistical Models of Network Structure

Readings

  • Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191. link
  • Morris, M., Handcock, M. S., & Hunter, D. R. (2008). Specification of exponential-family random graph models: terms and computational aspects. Journal of Statistical Software, 24(4), 1548. link
  • Pattison, P., & Robins, G. (2002). Neighborhood–based models for social networks. Sociological Methodology, 32(1), 301-337. link

Further Reading

  • Lusher D., Wang, P., Brennecke, J., Brailly J., Faye, M., Gallagher, C. (2021). Advances in Exponential Random Graph Models. In R. Light and J. Moody (Eds.) The Oxford Handbook of Social Networks, Oxford University Press. link
  • Orsini, C., Dankulov, M. M., Colomer-de-Simón, P., Jamakovic, A., Mahadevan, P., Vahdat, A., … & Krioukov, D. (2015). Quantifying randomness in real networks. Nature communications, 6(1), 8627. link

Week 10, June 3: No Class (Traveling)