SOCIOL 208A Reading Schedule

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All lecture notes linked below are hosted on the companion course website: olizardo.github.io/SOCIOL208.

1 Week 1: Basic SNA Concepts

1.1 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

1.2 Other Material

1.3 Lecture Notes

2 Week 2: Centrality

2.1 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

2.2 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

2.3 Lecture Notes

3 Week 3: Ego Networks

3.1 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

3.2 Lecture Notes

4 Week 4: Status and Prestige

4.1 Readings

  • 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

4.2 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

4.3 Lecture Notes

5 Week 5: Similarity, Roles, and Structural Equivalence

5.1 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

5.2 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

5.3 Cheat Sheets

  • Centrality, Status, and Node Similarity Cheat Sheet.
  • Chroł, B & Bojanowski, M. (2018). Proximity-based Methods for Link Prediction. link

5.4 Lecture Notes

6 Week 6: Community Detection — Foundations

6.1 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
  • 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
  • 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
  • Newman, M. E. (2006). Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582. link
  • Melamed, D. (2015). Communities of classes: A network approach to social mobility. Research in Social Stratification and Mobility, 41, 56-65. link

6.2 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

6.3 Further (Mathy) Reading

  • Fortunato, S. (2010). Community Detection in Graphs. Physics Reports, 486(3-5), 75-174. link
  • Leicht, E. A., and Newman, M. E. (2008). Community Structure in Directed Networks. Physical Review Letters 100, 118703. 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

6.4 Lecture Notes

7 Week 7: Advanced Community Detection Methods

7.1 Readings

  • Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814-818. link
  • Ahn, Y.-Y., Bagrow, J. P., & Lehmann, S. (2010). Link communities reveal multiscale complexity in networks. Nature, 466(7307), 761-764. link
  • Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences, 101(9), 2658-2663. link
  • Lancichinetti, A., Fortunato, S., & Kertész, J. (2009). Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 11(3), 033015. link
  • Barber, M. J. (2007). Modularity and community detection in bipartite networks. Physical Review E, 76(6), 066102. link

7.2 Further (Bipartite/Two-Mode Extensions)

  • Wu, G., Gu, C., & Yang, H. (2022). A spectral method of modularity for community detection in bipartite networks. Europhysics Letters, 137(3), 31001. link
  • Murata, T. (2009). Community division of heterogeneous networks. In Complex Sciences: First International Conference, Complex 2009 (pp. 1011-1022). Springer.
  • Suzuki, K., & Wakita, K. (2009). Extracting multi-facet community structure from bipartite networks. In 2009 International Conference on Computational Science and Engineering (Vol. 4, pp. 312-319). IEEE. link

7.3 Lecture Notes

8 Week 8: Analyzing Two-Mode Networks

8.1 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

8.2 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

8.3 Other Material

  • Murphy, Phil, and Brendan Knapp. (2018). Bipartite/two-mode networks in igraph. RPubs link
  • 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

8.4 Lecture Notes

9 Week 9: Statistical Network Models I — Random Graphs, QAP, and Permutation Methods

9.1 Readings

  • Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359-381. link
  • Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296(5569), 910-913. 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
  • Borgatti, S. P., Agneessens, F., Johnson, J. C., & Everett, M. G. (2024). Analyzing Social Networks Using R. SAGE Publications Ltd.

9.2 Further (Two-Mode Null Models)

  • Neal, Z. P., Cadieux, A., Garlaschelli, D., Gotelli, N. J., Saracco, F., Squartini, T., Shutters, S. T., Ulrich, W., Wang, G., & Strona, G. (2024). Pattern detection in bipartite networks: A review of terminology, applications, and methods. PLOS Complex Systems, 1(2), e0000010. link

9.3 Lecture Notes

10 Week 10: Statistical Network Models II — Exponential Random Graph Models (ERGMs)

10.1 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
  • Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.

10.2 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

10.3 Lecture Notes